Search Results

Search found 1302 results on 53 pages for 'ai pragma'.

Page 1/53 | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >

  • Setting up a local AI server - easy with Solaris 11

    - by Stefan Hinker
    Many things are new in Solaris 11, Autoinstall is one of them.  If, like me, you've known Jumpstart for the last 2 centuries or so, you'll have to start from scratch.  Well, almost, as the concepts are similar, and it's not all that difficult.  Just new. I wanted to have an AI server that I could use for demo purposes, on the train if need be.  That answers the question of hardware requirements: portable.  But let's start at the beginning. First, you need an OS image, of course.  In the new world of Solaris 11, it is now called a repository.  The original can be downloaded from the Solaris 11 page at Oracle.   What you want is the "Oracle Solaris 11 11/11 Repository Image", which comes in two parts that can be combined using cat.  MD5 checksums for these (and all other downloads from that page) are available closer to the top of the page. With that, building the repository is quick and simple: # zfs create -o mountpoint=/export/repo rpool/ai/repo # zfs create rpool/ai/repo/s11 # mount -o ro -F hsfs /tmp/sol-11-1111-repo-full.iso /mnt # rsync -aP /mnt/repo /export/repo/s11 # umount /mnt # pkgrepo rebuild -s /export/repo/sol11/repo # zfs snapshot rpool/ai/repo/sol11@fcs # pkgrepo info -s /export/repo/sol11/repo PUBLISHER PACKAGES STATUS UPDATED solaris 4292 online 2012-03-12T20:47:15.378639Z That's all there's to it.  Let's make a snapshot, just to be on the safe side.  You never know when one will come in handy.  To use this repository, you could just add it as a file-based publisher: # pkg set-publisher -g file:///export/repo/sol11/repo solaris In case I'd want to access this repository through a (virtual) network, i'll now quickly activate the repository-service: # svccfg -s application/pkg/server \ setprop pkg/inst_root=/export/repo/sol11/repo # svccfg -s application/pkg/server setprop pkg/readonly=true # svcadm refresh application/pkg/server # svcadm enable application/pkg/server That's all you need - now point your browser to http://localhost/ to view your beautiful repository-server. Step 1 is done.  All of this, by the way, is nicely documented in the README file that's contained in the repository image. Of course, we already have updates to the original release.  You can find them in MOS in the Oracle Solaris 11 Support Repository Updates (SRU) Index.  You can simply add these to your existing repository or create separate repositories for each SRU.  The individual SRUs are self-sufficient and incremental - SRU4 includes all updates from SRU2 and SRU3.  With ZFS, you can also get both: A full repository with all updates and at the same time incremental ones up to each of the updates: # mount -o ro -F hsfs /tmp/sol-11-1111-sru4-05-incr-repo.iso /mnt # pkgrecv -s /mnt/repo -d /export/repo/sol11/repo '*' # umount /mnt # pkgrepo rebuild -s /export/repo/sol11/repo # zfs snapshot rpool/ai/repo/sol11@sru4 # zfs set snapdir=visible rpool/ai/repo/sol11 # svcadm restart svc:/application/pkg/server:default The normal repository is now updated to SRU4.  Thanks to the ZFS snapshots, there is also a valid repository of Solaris 11 11/11 without the update located at /export/repo/sol11/.zfs/snapshot/fcs . If you like, you can also create another repository service for each update, running on a separate port. But now lets continue with the AI server.  Just a little bit of reading in the dokumentation makes it clear that we will need to run a DHCP server for this.  Since I already have one active (for my SunRay installation) and since it's a good idea to have these kinds of services separate anyway, I decided to create this in a Zone.  So, let's create one first: # zfs create -o mountpoint=/export/install rpool/ai/install # zfs create -o mountpoint=/zones rpool/zones # zonecfg -z ai-server zonecfg:ai-server> create create: Using system default template 'SYSdefault' zonecfg:ai-server> set zonepath=/zones/ai-server zonecfg:ai-server> add dataset zonecfg:ai-server:dataset> set name=rpool/ai/install zonecfg:ai-server:dataset> set alias=install zonecfg:ai-server:dataset> end zonecfg:ai-server> commit zonecfg:ai-server> exit # zoneadm -z ai-server install # zoneadm -z ai-server boot ; zlogin -C ai-server Give it a hostname and IP address at first boot, and there's the Zone.  For a publisher for Solaris packages, it will be bound to the "System Publisher" from the Global Zone.  The /export/install filesystem, of course, is intended to be used by the AI server.  Let's configure it now: #zlogin ai-server root@ai-server:~# pkg install install/installadm root@ai-server:~# installadm create-service -n x86-fcs -a i386 \ -s pkg://solaris/install-image/[email protected],5.11-0.175.0.0.0.2.1482 \ -d /export/install/fcs -i 192.168.2.20 -c 3 With that, the core AI server is already done.  What happened here?  First, I installed the AI server software.  IPS makes that nice and easy.  If necessary, it'll also pull in the required DHCP-Server and anything else that might be missing.  Watch out for that DHCP server software.  In Solaris 11, there are two different versions.  There's the one you might know from Solaris 10 and earlier, and then there's a new one from ISC.  The latter is the one we need for AI.  The SMF service names of both are very similar.  The "old" one is "svc:/network/dhcp-server:default". The ISC-server comes with several SMF-services. We at least need "svc:/network/dhcp/server:ipv4".  The command "installadm create-service" creates the installation-service. It's called "x86-fcs", serves the "i386" architecture and gets its boot image from the repository of the system publisher, using version 5.11,5.11-0.175.0.0.0.2.1482, which is Solaris 11 11/11.  (The option "-a i386" in this example is optional, since the installserver itself runs on a x86 machine.) The boot-environment for clients is created in /export/install/fcs and the DHCP-server is configured for 3 IP-addresses starting at 192.168.2.20.  This configuration is stored in a very human readable form in /etc/inet/dhcpd4.conf.  An AI-service for SPARC systems could be created in the very same way, using "-a sparc" as the architecture option. Now we would be ready to register and install the first client.  It would be installed with the default "solaris-large-server" using the publisher "http://pkg.oracle.com/solaris/release" and would query it's configuration interactively at first boot.  This makes it very clear that an AI-server is really only a boot-server.  The true source of packets to install can be different.  Since I don't like these defaults for my demo setup, I did some extra config work for my clients. The configuration of a client is controlled by manifests and profiles.  The manifest controls which packets are installed and how the filesystems are layed out.  In that, it's very much like the old "rules.ok" file in Jumpstart.  Profiles contain additional configuration like root passwords, primary user account, IP addresses, keyboard layout etc.  Hence, profiles are very similar to the old sysid.cfg file. The easiest way to get your hands on a manifest is to ask the AI server we just created to give us it's default one.  Then modify that to our liking and give it back to the installserver to use: root@ai-server:~# mkdir -p /export/install/configs/manifests root@ai-server:~# cd /export/install/configs/manifests root@ai-server:~# installadm export -n x86-fcs -m orig_default \ -o orig_default.xml root@ai-server:~# cp orig_default.xml s11-fcs.small.local.xml root@ai-server:~# vi s11-fcs.small.local.xml root@ai-server:~# more s11-fcs.small.local.xml <!DOCTYPE auto_install SYSTEM "file:///usr/share/install/ai.dtd.1"> <auto_install> <ai_instance name="S11 Small fcs local"> <target> <logical> <zpool name="rpool" is_root="true"> <filesystem name="export" mountpoint="/export"/> <filesystem name="export/home"/> <be name="solaris"/> </zpool> </logical> </target> <software type="IPS"> <destination> <image> <!-- Specify locales to install --> <facet set="false">facet.locale.*</facet> <facet set="true">facet.locale.de</facet> <facet set="true">facet.locale.de_DE</facet> <facet set="true">facet.locale.en</facet> <facet set="true">facet.locale.en_US</facet> </image> </destination> <source> <publisher name="solaris"> <origin name="http://192.168.2.12/"/> </publisher> </source> <!-- By default the latest build available, in the specified IPS repository, is installed. If another build is required, the build number has to be appended to the 'entire' package in the following form: <name>pkg:/[email protected]#</name> --> <software_data action="install"> <name>pkg:/[email protected],5.11-0.175.0.0.0.2.0</name> <name>pkg:/group/system/solaris-small-server</name> </software_data> </software> </ai_instance> </auto_install> root@ai-server:~# installadm create-manifest -n x86-fcs -d \ -f ./s11-fcs.small.local.xml root@ai-server:~# installadm list -m -n x86-fcs Manifest Status Criteria -------- ------ -------- S11 Small fcs local Default None orig_default Inactive None The major points in this new manifest are: Install "solaris-small-server" Install a few locales less than the default.  I'm not that fluid in French or Japanese... Use my own package service as publisher, running on IP address 192.168.2.12 Install the initial release of Solaris 11:  pkg:/[email protected],5.11-0.175.0.0.0.2.0 Using a similar approach, I'll create a default profile interactively and use it as a template for a few customized building blocks, each defining a part of the overall system configuration.  The modular approach makes it easy to configure numerous clients later on: root@ai-server:~# mkdir -p /export/install/configs/profiles root@ai-server:~# cd /export/install/configs/profiles root@ai-server:~# sysconfig create-profile -o default.xml root@ai-server:~# cp default.xml general.xml; cp default.xml mars.xml root@ai-server:~# cp default.xml user.xml root@ai-server:~# vi general.xml mars.xml user.xml root@ai-server:~# more general.xml mars.xml user.xml :::::::::::::: general.xml :::::::::::::: <!DOCTYPE service_bundle SYSTEM "/usr/share/lib/xml/dtd/service_bundle.dtd.1"> <service_bundle type="profile" name="sysconfig"> <service version="1" type="service" name="system/timezone"> <instance enabled="true" name="default"> <property_group type="application" name="timezone"> <propval type="astring" name="localtime" value="Europe/Berlin"/> </property_group> </instance> </service> <service version="1" type="service" name="system/environment"> <instance enabled="true" name="init"> <property_group type="application" name="environment"> <propval type="astring" name="LANG" value="C"/> </property_group> </instance> </service> <service version="1" type="service" name="system/keymap"> <instance enabled="true" name="default"> <property_group type="system" name="keymap"> <propval type="astring" name="layout" value="US-English"/> </property_group> </instance> </service> <service version="1" type="service" name="system/console-login"> <instance enabled="true" name="default"> <property_group type="application" name="ttymon"> <propval type="astring" name="terminal_type" value="vt100"/> </property_group> </instance> </service> <service version="1" type="service" name="network/physical"> <instance enabled="true" name="default"> <property_group type="application" name="netcfg"> <propval type="astring" name="active_ncp" value="DefaultFixed"/> </property_group> </instance> </service> <service version="1" type="service" name="system/name-service/switch"> <property_group type="application" name="config"> <propval type="astring" name="default" value="files"/> <propval type="astring" name="host" value="files dns"/> <propval type="astring" name="printer" value="user files"/> </property_group> <instance enabled="true" name="default"/> </service> <service version="1" type="service" name="system/name-service/cache"> <instance enabled="true" name="default"/> </service> <service version="1" type="service" name="network/dns/client"> <property_group type="application" name="config"> <property type="net_address" name="nameserver"> <net_address_list> <value_node value="192.168.2.1"/> </net_address_list> </property> </property_group> <instance enabled="true" name="default"/> </service> </service_bundle> :::::::::::::: mars.xml :::::::::::::: <!DOCTYPE service_bundle SYSTEM "/usr/share/lib/xml/dtd/service_bundle.dtd.1"> <service_bundle type="profile" name="sysconfig"> <service version="1" type="service" name="network/install"> <instance enabled="true" name="default"> <property_group type="application" name="install_ipv4_interface"> <propval type="astring" name="address_type" value="static"/> <propval type="net_address_v4" name="static_address" value="192.168.2.100/24"/> <propval type="astring" name="name" value="net0/v4"/> <propval type="net_address_v4" name="default_route" value="192.168.2.1"/> </property_group> <property_group type="application" name="install_ipv6_interface"> <propval type="astring" name="stateful" value="yes"/> <propval type="astring" name="stateless" value="yes"/> <propval type="astring" name="address_type" value="addrconf"/> <propval type="astring" name="name" value="net0/v6"/> </property_group> </instance> </service> <service version="1" type="service" name="system/identity"> <instance enabled="true" name="node"> <property_group type="application" name="config"> <propval type="astring" name="nodename" value="mars"/> </property_group> </instance> </service> </service_bundle> :::::::::::::: user.xml :::::::::::::: <!DOCTYPE service_bundle SYSTEM "/usr/share/lib/xml/dtd/service_bundle.dtd.1"> <service_bundle type="profile" name="sysconfig"> <service version="1" type="service" name="system/config-user"> <instance enabled="true" name="default"> <property_group type="application" name="root_account"> <propval type="astring" name="login" value="root"/> <propval type="astring" name="password" value="noIWillNotTellYouMyPasswordNotEvenEncrypted"/> <propval type="astring" name="type" value="role"/> </property_group> <property_group type="application" name="user_account"> <propval type="astring" name="login" value="stefan"/> <propval type="astring" name="password" value="noIWillNotTellYouMyPasswordNotEvenEncrypted"/> <propval type="astring" name="type" value="normal"/> <propval type="astring" name="description" value="Stefan Hinker"/> <propval type="count" name="uid" value="12345"/> <propval type="count" name="gid" value="10"/> <propval type="astring" name="shell" value="/usr/bin/bash"/> <propval type="astring" name="roles" value="root"/> <propval type="astring" name="profiles" value="System Administrator"/> <propval type="astring" name="sudoers" value="ALL=(ALL) ALL"/> </property_group> </instance> </service> </service_bundle> root@ai-server:~# installadm create-profile -n x86-fcs -f general.xml root@ai-server:~# installadm create-profile -n x86-fcs -f user.xml root@ai-server:~# installadm create-profile -n x86-fcs -f mars.xml \ -c ipv4=192.168.2.100 root@ai-server:~# installadm list -p Service Name Profile ------------ ------- x86-fcs general.xml mars.xml user.xml root@ai-server:~# installadm list -n x86-fcs -p Profile Criteria ------- -------- general.xml None mars.xml ipv4 = 192.168.2.100 user.xml None Here's the idea behind these files: "general.xml" contains settings valid for all my clients.  Stuff like DNS servers, for example, which in my case will always be the same. "user.xml" only contains user definitions.  That is, a root password and a primary user.Both of these profiles will be valid for all clients (for now). "mars.xml" defines network settings for an individual client.  This profile is associated with an IP-Address.  For this to work, I'll have to tweak the DHCP-settings in the next step: root@ai-server:~# installadm create-client -e 08:00:27:AA:3D:B1 -n x86-fcs root@ai-server:~# vi /etc/inet/dhcpd4.conf root@ai-server:~# tail -5 /etc/inet/dhcpd4.conf host 080027AA3DB1 { hardware ethernet 08:00:27:AA:3D:B1; fixed-address 192.168.2.100; filename "01080027AA3DB1"; } This completes the client preparations.  I manually added the IP-Address for mars to /etc/inet/dhcpd4.conf.  This is needed for the "mars.xml" profile.  Disabling arbitrary DHCP-replies will shut up this DHCP server, making my life in a shared environment a lot more peaceful ;-)Now, I of course want this installation to be completely hands-off.  For this to work, I'll need to modify the grub boot menu for this client slightly.  You can find it in /etc/netboot.  "installadm create-client" will create a new boot menu for every client, identified by the client's MAC address.  The template for this can be found in a subdirectory with the name of the install service, /etc/netboot/x86-fcs in our case.  If you don't want to change this manually for every client, modify that template to your liking instead. root@ai-server:~# cd /etc/netboot root@ai-server:~# cp menu.lst.01080027AA3DB1 menu.lst.01080027AA3DB1.org root@ai-server:~# vi menu.lst.01080027AA3DB1 root@ai-server:~# diff menu.lst.01080027AA3DB1 menu.lst.01080027AA3DB1.org 1,2c1,2 < default=1 < timeout=10 --- > default=0 > timeout=30 root@ai-server:~# more menu.lst.01080027AA3DB1 default=1 timeout=10 min_mem64=0 title Oracle Solaris 11 11/11 Text Installer and command line kernel$ /x86-fcs/platform/i86pc/kernel/$ISADIR/unix -B install_media=htt p://$serverIP:5555//export/install/fcs,install_service=x86-fcs,install_svc_addre ss=$serverIP:5555 module$ /x86-fcs/platform/i86pc/$ISADIR/boot_archive title Oracle Solaris 11 11/11 Automated Install kernel$ /x86-fcs/platform/i86pc/kernel/$ISADIR/unix -B install=true,inst all_media=http://$serverIP:5555//export/install/fcs,install_service=x86-fcs,inst all_svc_address=$serverIP:5555,livemode=text module$ /x86-fcs/platform/i86pc/$ISADIR/boot_archive Now just boot the client off the network using PXE-boot.  For my demo purposes, that's a client from VirtualBox, of course.  That's all there's to it.  And despite the fact that this blog entry is a little longer - that wasn't that hard now, was it?

    Read the article

  • techniques for an AI for a highly cramped turn-based tactics game

    - by Adam M.
    I'm trying to write an AI for a tactics game in the vein of Final Fantasy Tactics or Vandal Hearts. I can't change the game rules in any way, only upgrade the AI. I have experience programming AI for classic board games (basically minimax and its variants), but I think the branching factor is too great for the approach to be reasonable here. I'll describe the game and some current AI flaws that I'd like to fix. I'd like to hear ideas for applicable techniques. I'm a decent enough programmer, so I only need the ideas, not an implementation (though that's always appreciated). I'd rather not expend effort chasing (too many) dead ends, so although speculation and brainstorming are good and probably helpful, I'd prefer to hear from somebody with actual experience solving this kind of problem. For those who know it, the game is the land battle mini-game in Sid Meier's Pirates! (2004) and you can skim/skip the next two paragraphs. For those who don't, here's briefly how it works. The battle is turn-based and takes place on a 16x16 grid. There are three terrain types: clear (no hindrance), forest (hinders movement, ranged attacks, and sight), and rock (impassible, but does not hinder attacks or sight). The map is randomly generated with roughly equal amounts of each type of terrain. Because there are many rock and forest tiles, movement is typically very cramped. This is tactically important. The terrain is not flat; higher terrain gives minor bonuses. The terrain is known to both sides. The player is always the attacker and the AI is always the defender, so it's perfectly valid for the AI to set up a defensive position and just wait. The player wins by killing all defenders or by getting a unit to the city gates (a tile on the other side of the map). There are very few units on each side, usually 4-8. Because of this, it's crucial not to take damage without gaining some advantage from it. Units can take multiple actions per turn. All units on one side move before any units on the other side. Order of execution is important, and interleaving of actions between units is often useful. Units have melee and ranged attacks. Melee attacks vary widely in strength; ranged attacks have the same strength but vary in range. The main challenges I face are these: Lots of useful move combinations start with a "useless" move that gains no immediate advantage, or even loses advantage, in order to set up a powerful flank attack in the future. And, since the player units are stronger and have longer range, the AI pretty much always has to take some losses before they can start to gain kills. The AI must be able to look ahead to distinguish between sacrificial actions that provide a future benefit and those that don't. Because the terrain is so cramped, most of the tactics come down to achieving good positioning with multiple units that work together to defend an area. For instance, two defenders can often dominate a narrow pass by positioning themselves so an enemy unit attempting to pass must expose itself to a flank attack. But one defender in the same pass would be useless, and three units can defend a slightly larger pass. Etc. The AI should be able to figure out where the player must go to reach the city gates and how to best position its few units to cover the approaches, shifting, splitting, or combining them appropriately as the player moves. Because flank attacks are extremely deadly (and engineering flank attacks is key to the player strategy), the AI should be competent at moving its units so that they cover each other's flanks unless the sacrifice of a unit would give a substantial benefit. They should also be able to force flank attacks on players, for instance by threatening a unit from two different directions such that responding to one threat exposes the flank to the other. The AI should attack if possible, but sometimes there are no good ways to approach the player's position. In that case, the AI should be able to recognize this and set up a defensive position of its own. But the AI shouldn't be vulnerable to a trivial exploit where the player repeatedly opens and closes a hole in his defense and shoots at the AI as it approaches and retreats. That is, the AI should ideally be able to recognize that the player is capable of establishing a solid defense of an area, even if the defense is not currently in place. (I suppose if a good unit allocation algorithm existed, as needed for the second bullet point, the AI could run it on the player units to see where they could defend.) Because it's important to choose a good order of action and interleave actions between units, it's not as simple as just finding the best move for each unit in turn. All of these can be accomplished with a minimax search in theory, but the search space is too large, so specialized techniques are needed. I thought about techniques such as influence mapping, but I don't see how to use the technique to great effect. I thought about assigning goals to the units. This can help them work together in some limited way, and the problem of "how do I accomplish this goal?" is easier to solve than "how do I win this battle?", but assigning good goals is a hard problem in itself, because it requires knowing whether the goal is achievable and whether it's a good use of resources. So, does anyone have specific ideas for techniques that can help cleverize this AI? Update: I found a related question on Stackoverflow: http://stackoverflow.com/questions/3133273/ai-for-a-final-fantasy-tactics-like-game The selected answer gives a decent approach to choosing between alternative actions, but it doesn't seem to have much ability to look into the future and discern beneficial sacrifices from wasteful ones. It also focuses on a single unit at a time and it's not clear how it could be extended to support cooperation between units in defending or attacking.

    Read the article

  • #pragma init and #pragma fini using gcc compiler on linux

    - by Josh
    I would like to build some code which calls some code on loadup of the shared library. I thought i would do it like this: #pragma init(my_init) static void my_init () { //do-something } int add (int a,int b) { return a+b; } So when i build that code with gcc -fPIC -g -c -Wall tt.c It returns gcc -fPIC -g -c -Wall tt.c tt.c:2: warning: ignoring #pragma init tt.c:4: warning: ‘my_init’ defined but not used So its warning my #pragmas. I tried this in real code and my code aborted because a function hadn't been called in the pragma section because it was ignored. How do i get gcc to use these #pragma init and fini statemets?

    Read the article

  • Can anyone recommend an AI sandbox?

    - by user19433
    I'm passionate person, who has been around AI from a long time [1] but never going in deep enough. Now it's time! I've been really looking for some way to concentrate on AI coding but couldn't succeeded to find an AI environment I can focus on. I just want to use an AI sandbox environment which would let me have tools like: visibility information character controller able to easily define a level, with obstacles of course physics collider management triggers management don't need to be a shiny, eye candy graphical render : this is about pathfinding, tactical reasoning, etc.. I have tried : Unreal Dev Kit : while the new release announce is about C++ coding, this is about external tools and will be released in 2013 Cry Engine : really interesting as AI is presents here but coding with it appears to be an hell: did I got it wrong ? Half Life source, C4, Torque, Dx Studio : either quite old, not very useful or costly these imply to dig in documentation (when provided) to code everything, graphics included. Unity 3D : the most promising platform. While you also need to create your own environment, there are lot of examples. The disadvantage is, in addition to spend time to have this env. working, is the languages choice : C#, Javascript or Boo. C# is not that hard, but this implies you'll allways have to convert papers (I love those from Lars Linden) books codes, or anything you can have in Aigamedev are most often in C++. This is extra work. I've look at "Simple Path", the very good Arong Greenberg work but no source provided and AngryAnt work. AI Sandbox : this seems to be exactly what as AI coder I want to use. I saw some preview but from 2009 we still don't know what it will be about precisely, will it be opensource or free (I strongly doubt), will I be able to buy it? will it really provide me tools I need to focus on AI ? That being said, what is the best environment to be able to focus on AI coding only, is it even possible?

    Read the article

  • Help with Strategy-game AI

    - by f20k
    Hi, I am developing a strategy-game AI (think: Final Fantasy Tactics), and I am having trouble coming up for the design of the AI. My main problem is determining which is the optimal thing for it to do. First let me describe the priority of what action I would like the AI to take: Kill nearest player unit Fulfill primary directive (kill all player units, kill target unit, survive for x turns) Heal ally unit / cast buffer Now the AI can do the following in its turn: Move - {Attack / Ability / Item} (either attack or ability or item) {Attack / Ability / Item} - Move Move closer (if targets not in range) {Attack / Ability / Item} (if move not available) Notes Abilities have various ranges / effects / costs / effects. Each ai unit has maybe 5-10 abilities to choose from. The AI will prioritize killing over safety unless its directive is to survive for x turns. It also doesn't care about ability cost much. While a player may want to save a big spell for later, the AI will most likely use it asap. Movement is on a (hex) grid num of player units: 3-6 num of ai units: 3-7 or more. Probably max 10. AI and player take turns controlling ONE unit, instead of all at the same time. Platform is Android (if program doesnt respond after some time, there will be a popup saying to Force Quit or Wait - which looks really bad!). Now comes the questions: The best ability to use would obviously be the one that hits the most targets for the most damage. But since each ability has different ranges, I won't know if they are in range without exploring each possible place I can move to. One solution would be to go through each possible places to move to, determine the optimal attack at that location - which gives me a list of optimal moves for each location. Then choose the optimal out of the list and execute it. But this will take a lot of CPU time. Is there a better solution? My current idea is to move as close as possible towards the closest, largest group of people, and determine the optimal attack/ability from there. I think this would be a lot less work for the CPU and still allow for wide-range attacks. Its sub-optimal but the AI will still seem 'smart'. Other notes/questions: Am I over-thinking/over-complicating it? Better solution? I am open to all sorts of suggestions I have taken a look at the spell-casting question, but it doesn't take into account the movement - so perhaps use that algo for each possible move location? The top answer mentioned it wasn't great for area-of-effect and group fights - so maybe requires more tweaking? Please, if you mention a graph/tree, let me know basically how to use it. E.g. Node means ability, level corresponds to damage, then search for the deepest node.

    Read the article

  • AI agents with FSM: a question regarding this

    - by Prog
    Finite State Machines implemented with the State design pattern are a common way to design AI agents. I am familiar with the State design pattern and know how to implement it. However I have a question regarding how this is used in games to design AI agents. Please consider a class Monster that represents an AI agent. Simplified it looks like this: class Monster{ State state; // other fields omitted public void update(){ // called every game-loop cycle state.execute(this); } public void setState(State state){ this.state = state; } // irrelevant stuff omitted } There are several State subclasses that implement execute() differently. So far classic State pattern. Here's my question: AI agents are subject to environmental effects and other objects communicating with them. For example an AI agent might tell another AI agent to attack (i.e. agent.attack()). Or a fireball might tell an AI agent to fall down. This means that the agent must have methods such as attack() and fallDown(), or commonly some message receiving mechanism to understand such messages. My question is divided to two parts: 1- Please say if this is correct: With an FSM, the current State of the agent should be the one taking care of such method calls - i.e. the agent delegates to the current state upon every event. Correct? Or wrong? 2- If correct, than how is this done? Are all states obligated by their superclass) to implement methods such as attack(), fallDown() etc., so the agent can always delegate to them on almost every event? Or is it done in some other way?

    Read the article

  • Scripting a sophisticated RTS AI with Lua

    - by T. Webster
    I'm planning to develop a somewhat sophisticated RTS AI (eg see BWAPI). have experience programming, but none in game development, so it seems easiest to start by scripting the AI of an existing game I've played, Warhammer 40k: Dawn of War (2004). As far as I can tell, the game AI is scripted with some variant of Lua (by the file extension .ai or .scar). The online documentation is sparse and the community isn't active anymore. I'd like to get some idea of the difficulty of this undertaking. Is it practical with a scripting language like Lua to develop a RTS AI that includes FSMs, decision trees, case-based reasoning, and transposition tables? If someone has any experience scripting Dawn of War, that would also help.

    Read the article

  • How Can I Improve This Card-Game AI?

    - by James Burgess
    Let me get this out there before anything else: this is a learning exercise for me. I am not a game developer by trade or hobby (at least, not seriously) and am purely delving into some AI- and 3D-related topics to broaden my horizons a bit. As part of the learning experience, I thought I'd have a go at developing a basic card game AI. I selected Pit as the card game I was going to attempt to emulate (specifically, the 'bull and bear' variation of the game as mentioned in the link above). Unfortunately, the rule-set that I'm used to playing with (an older version of the game) isn't described. The basics of it are: The number of commodities played with is equal to the number of players. The bull and bear cards are included. All but two players receive 8 cards, two receive 9 cards. A player can win the round with 7 + bull, 8, or 8 + bull (receiving double points). The bear is a penalty card. You can trade up to a maximum of 4 cards at a time. They must all be of the same type, but can optionally include the bull or bear (so, you could trade A, A, A, Bull - but not A, B, A, Bull). For those who have played the card game, it will probably have been as obvious to you as it was to me that given the nature of the game, gameplay would seem to resemble a greedy algorithm. With this in mind, I thought it might simplify my AI experience somewhat. So, here's what I've come up with for a basic AI player to play Pit... and I'd really just like any form of suggestion (from improvements to reading materials) relating to it. Here it is in something vaguely pseudo-code-ish ;) While AI does not hold 7 similar + bull, 8 similar, or 8 similar + bull, do: 1. Establish 'target' hand, by seeing which card AI holds the most of. 2. Prepare to trade next-most-numerous card type in a trade (max. held, or 4, whichever is fewer) 3. If holding the bear, add to (if trading <=3 cards) or replace in (if trading 4 cards) hand. 4. Offer cards for trade. 5. If cards are accepted for trade within X turns, continue (clearing 'failed card types'). Otherwise: a. If only one card remains in the trade, go to #6. Otherwise: i. Remove one non-penalty card from the trade. ii. Return to #5. 6. Add card type to temporary list of failed card types. 7. Repeat from #2 (excluding 'failed card types'). I'm aware this is likely to be a sub-optimal way of solving the problem, but that's why I'm posting this question. Are there any AI- or algorithm-related concepts that I've missed and should be incorporating to make a better AI? Additionally, what are the flaws with my AI at present (I'm well aware it's probably far from complete)? Thanks in advance!

    Read the article

  • Doing a passable 4X game AI

    - by Extrakun
    I am coding a rather "simple" 4X game (if a 4X game can be simple). It's indie in scope, and I am wondering if there's anyway to come up with a passable AI without having me spending months coding on it. The game has three major decision making portions; spending of production points, spending of movement points and spending of tech points (basically there are 3 different 'currency', currency unspent at end of turn is not saved) Spend Production Points Upgrade a planet (increase its tech and production) Build ships (3 types) Move ships from planets to planets (costing Movement Points) Move to attack Move to fortify Research Tech (can partially research a tech i.e, as in Master of Orion) The plan for me right now is a brute force approach. There are basically 4 broad options for the player - Upgrade planet(s) to its his production and tech output Conquer as many planets as possible Secure as many planets as possible Get to a certain tech as soon as possible For each decision, I will iterate through the possible options and come up with a score; and then the AI will choose the decision with the highest score. Right now I have no idea how to 'mix decisions'. That is, for example, the AI wishes to upgrade and conquer planets at the same time. I suppose I can have another logic which do a brute force optimization on a combination of those 4 decisions.... At least, that's my plan if I can't think of anything better. Is there any faster way to make a passable AI? I don't need a very good one, to rival Deep Blue or such, just something that has the illusion of intelligence. This is my first time doing an AI on this scale, so I dare not try something too grand too. So far I have experiences with FSM, DFS, BFS and A*

    Read the article

  • Combining pathfinding with global AI objectives

    - by V_Programmer
    I'm making a turn-based strategy game using Java and LibGDX. Now I want to code the AI. I haven't written the AI code yet. I've simply designed it. The AI will have two components, one focused in tactics and resource management (create troops, determine who have strategical advantage, detect important objectives, etc) and a individual component, focused in assign the work to each unit, examine its possibilites and move the unit. Now I'm facing an important problem. The map where the action take place is a grid-based map. Each terrain has different movement cost. I read about pathfinding and I think A* is a very good option to determine a good route between two points. However, imagine I have an unit with movement = 5 (i.e, it can move 5 tiles of movement cost = 1). My tactical AI has found an objective at a distance d = 20 tiles (Manhattan distance) from my unit. My problem is the following: the unit won't be able to reach the objective in one turn. So the AI will have to store a list of position and execute them in various turns. I don't know how to solve this. PS. In my unit code, I have a list called "selectionMarks" which stores all the possible places where the unit can go in this turn. This places are calculed recursively using a "getSelectionMarks" function. Any help is appreciated :D

    Read the article

  • Monster's AI in an Action-RPG

    - by Andrea Tucci
    I'm developing an action rpg with some University colleagues. We've gotton to the monsters' AI design and we would like to implement a sort of "utility-based AI" so we have a "thinker" that assigns a numeric value on all the monster's decisions and we choose the highest (or the most appropriate, depending on monster's iq) and assign it in the monster's collection of decisions (like a goal-driven design pattern) . One solution we found is to write a mathematical formula for each decision, with all the important parameters for evaluation (so for a spell-decision we might have mp,distance from player, player's hp etc). This formula also has coefficients representing some of monster's behaviour (in this way we can alterate formulas by changing coefficients). I've also read how "fuzzy logic" works; I was fascinated by it and by the many ways of expansion it has. I was wondering how we could use this technique to give our AI more semplicity, as in create evaluations with fuzzy rules such as IF player_far AND mp_high AND hp_high THEN very_Desiderable (for a spell having an high casting-time and consume high mp) and then 'defuzz' it. In this way it's also simple to create a monster behaviour by creating ad-hoc rules for every monster's IQ category. But is it correct using fuzzy logic in a game with many parameters like an rpg? Is there a way of merging these two techniques? Are there better AI design techniques for evaluating monster's chooses?

    Read the article

  • Platformer Enemy AI

    - by hayer
    I'm currently developing a platformer shooter. The game is multiplayer and while my net code could use some real work I have put that off for the time, so currently I'm trying to implement the AI. The game is pretty simple; Players run around on a map filled with a X amount of zombies that try to eat their brains, classic and overused I know. Weapons spawn at random intervals around the map. The problem is that the zombies, when they find their pray the have to follow it for some while.. And here is the problem, running the AI navcode seems to take for ever. So here is the ideas I have come up with so far Have the AI update at different intervals with a maximum of Y ms with no updates. Have the zombies assigned to groups of zombies. One is appointed the leader of the group who finds the way to the player - the rest just follows the leader. If the leader dies another one of the zombies in the group is appointed president of the zombie swarm. If there is less than five zombies in a group they try to meet up with other zombies.(Aka they are assigned to a different group and therefor a new leader) Multi-threading option one or two? For navigation I have some kinda navmesh(since the game is not tile-based) that tells the zombies where they can walk etc. If anyone else got some ideas on how to do navigation I would love some input. For LoS(zombie - player) I have split the map into grids. If the players grid is connected to the zombies grid(if I go with option two I would only need to check if leader zombies grid is connected to player, aka less checks) - if they are connected and there is more than 250ms since last check do a raytrace.. This is my first time programming AI so input on any field is appreciated.

    Read the article

  • What are the most common AI systems implemented in Tower Defense Games

    - by the_Dan
    I'm currently in the middle of researching on the various types of AI techniques used in tower defense type games. If someone could be help me in understanding the different types of techniques and their associated advantages. Using Google I already found several techniques. Random Map traversal Path finding e.g. Cost based Traversing Algorithms i.e. A* I have already found a great answer to this type of question with the below link, but I feel that this answer is tailored to FPS. If anyone could add to this and make it specific to tower defense games then I would be truly great-full. How is AI most commonly implemented in popular games? Example of such games would be: Radiant Defense Plant Vs Zombies - Not truly Intelligent, but there must be an AI system used right? Field Runners Edit: After further research I found an interesting book that may be useful: http://www.amazon.com/dp/0123747317/?tag=stackoverfl08-20

    Read the article

  • AI to move custom-shaped spaceships (shape affecting movement behaviour)

    - by kaoD
    I'm designing a networked turn based 3D-6DOF space fleet combat strategy game which relies heavily on ship customization. Let me explain the game a bit, since you need to know a bit about it to set the question. What I aim for is the ability to create your own fleet of ships with custom shapes and attached modules (propellers, tractor beams...) which would give advantages and disadvantages to each ship, so you have lots of different fleet distributions. E.g., long ship with two propellers at the side would let the ship spin around that plane easily, bigger ships would move slowly unless you place lots of propellers at the back (therefore spending more "construction" points and energy when moving, and it will only move fast towards that direction.) I plan to balance all the game around this feature. The game would revolve around two phases: orders and combat phase. During the orders phase, you command the different ships. When all players finish the order phase, the combat phase begins and the ship orders get resolved in real-time for some time, then the action pauses and there's a new orders phase. The problem comes when I think about player input. To move a ship, you need to turn on or off different propellers if you want to steer, travel forward, brake, rotate in place... These propellers don't have to work at their whole power, so you can achieve more movement combinations with less propellers. I think this approach is a bit boring. The player doesn't want to fiddle with motors or anything, you just want to MOVE and KILL. The way I intend the player to give orders to these ships is by a destination and a rotation, and then the AI would calculate the correct propeller power to achive that movement and rotation. Propulsion doesn't have to be the same throught the entire turn calculation (after the orders have been given) so it would be cool if the ships reacted as they move, adjusting the power of the propellers for their needs dynamically, but it may be too hard to implement and it's not really needed for the game to work. In both cases, how would that AI decide which propellers to activate for the best (or at least not worst) trajectory to be achieved? I though about some approaches: Learning AI: The ship types would learn about their movement by trial and error, adjusting their behaviour with more uses, and finally becoming "smart". I don't want to get involved THAT far in AI coding, and I think it can be frustrating for the player (even if you can let it learn without playing.) Pre-calculated timestep movement: Upon ship creation, ALL possible movements are calculated for each propeller configuration and power for a given delta-time. Memory intensive, ugly, bad. Pre-calculated trajectories: The same as above but not for each delta-time but the whole trajectory, which would then be fitted as much as possible. Requires a fixed propeller configuration for the whole combat phase and is still memory intensive, ugly and bad. Continuous brute forcing: The AI continously checks ALL possible propeller configurations throughout the entire combat phase, precalculates a few time steps and decides which is the best one based on that. Con: what's good now might not be that good later, and it's too CPU intensive, ugly, and bad too. Single brute forcing: Same as above, but only brute forcing at the beginning of the simulation, so it needs constant propeller configuration throughout the entire combat phase. Coninuous angle check: This is not a full movement method, but maybe a way to discard "stupid" propeller configurations. Given the current propeller's normal vector and the final one, you can approximate the power needed for the propeller based on the angle. You must do this continuously throughout the whole combat phase. I figured this one out recently so I didn't put in too much thought. A priori, it has the "what's good now might not be that good later" drawback too, and it doesn't care about the other propellers which may act together to make a better propelling configuration. I'm really stuck here. Any ideas?

    Read the article

  • Giving a Bomberman AI intelligent bomb placement

    - by Paul Manta
    I'm trying to implement an AI algorithm for Bomberman. Currently I have a working but not very smart rudimentary implementation (the current AI is overzealous in placing bombs). This is the first AI I've ever tried implementing and I'm a bit stuck. The more sophisticated algorithms I have in mind (the ones that I expect to make better decisions) are too convoluted to be good solutions. What general tips do you have for implementing a Bomberman AI? Are there radically different approaches for making the bot either more defensive or offensive? Edit: Current algorithm My current algorithm goes something like this (pseudo-code): 1) Try to place a bomb and then find a cell that is safe from all the bombs, including the one that you just placed. To find that cell, iterate over the four directions; if you can find any safe divergent cell and reach it in time (eg. if the direction is up or down, look for a cell that is found to the left or right of this path), then it's safe to place a bomb and move in that direction. 2) If you can't find and safe divergent cells, try NOT placing a bomb and look again. This time you'll only need to look for a safe cell in only one direction (you don't have to diverge from it). 3) If you still can't find a safe cell, don't do anything. for $(direction) in (up, down, left, right): place bomb at current location if (can find and reach divergent safe cell in current $(direction)): bomb = true move = $(direction) return for $(direction) in (up, down, left, right): do not place bomb at current location if (any safe cell in the current $(direction)): bomb = false move = $(direction) return else: bomb = false move = stay_put This algorithm makes the bot very trigger-happy (it'll place bombs very frequently). It doesn't kill itself, but it does have a habit of making itself vulnerable by going into dead ends where it can be blocked and killed by the other players. Do you have any suggestions on how I might improve this algorithm? Or maybe I should try something completely different? One of the problems with this algorithm is that it tends to leave the bot with very few (frequently just one) safe cells on which it can stand. This is because the bot leaves a trail of bombs behind it, as long as it doesn't kill itself. However, leaving a trail of bombs behind leaves few places where you can hide. If one of the other players or bots decide to place a bomb somewhere near you, it often happens that you have no place to hide and you die. I need a better way to decide when to place bombs.

    Read the article

  • Open Source AI Bot interfaces

    - by David Young
    What are some open source AI Bot interfaces? Similar to Pogamut 3 GameBots2004 for custom Unreal Tournament bots or Brood Wars API for Starcraft bots etc. If you could please post one AI bot interface per answer (make sure to provide a link) and give a brief summary as to the content of the blog posts. Please include what type of bot interface structure it is, client/server, server/server, etc e.g. BWAPI is client/server which emulates a real player

    Read the article

  • Simultaneous AI in turn based games

    - by Eduard Strehlau
    I want to hack together a roguelike. Now I thought about entity and world representation and got to a quite big problem. If you want all the AI to act simultaneously you would normally(in cellular automa for examble) just copy the cell buffer and let all action of indiviual cells depend on the copy. Actions which are not valid anymore after some cell before the cell you are currently operating on changed the original enviourment(blocking the path) are just ignored or reapplied with the "current"(between turns) environment. After all cells have acted you copy the current map to the buffer again. Now for an environment with complex AI and big(datawise) entities the copying would take too long. So I thought you could put every action and entity makes into a que(make no changes to the environment) and execute the whole que after everyone took their move. Every interaction on this que are realy interacting entities, so if a entity tries to attack another entity it sends a message to it, the consequences of the attack would be visible next turn, either by just examining the entity or asking the entity for data. This would remove problems like what happens if an entity dies middle in the cue but got actions or is messaged later on(all messages would go to null, and the messages from the entity would either just be sent or deleted(haven't decided yet) But what would happen if a monster spawns a fireball which by itself tracks the player(in the same turn). Should I add the fireball to the enviourment beforehand, so make a change to the environment before executing the action list or just add the ball to the "need updated" list as a special case so it doesn't exist in the environment and still operates on it, spawing after evaluating the action list? Are there any solutions or papers on this subject which I can take a look at? EDIT: I don't need information on writing a roguelike I need information on turn based ai in respective to a complex enviourment.

    Read the article

  • Implementing a multilanguage AI contest platform

    - by Alejandro Piad
    This is a followup to this question. To sum: I'm implementing an AI contest site, where each user may submit several AI implementations for different games. Think about Google AI Challenge but instead of just having a big event once a year, I would like it more on a league fashion, with all virtual players playing with each other every some close period of time. I want to support as many programming languages as possible. I've seen that contest sites (like codeforces) ask you to submit a source code and interact through stdin and stdout. The first question is: what is the best way of supporting multiple languages? As I see it, I can either ask people to upload some binary/script, and interact either through stdin/*stdout*, or sockets, or the file system; or ask people to submit source code, and wrap it with whatever is necessary for the interaction. I would like to skip the need to compile the code by myself (in the server, I mean), but I am willing to do it if its the "best" choice. I need to comunicate virtual players with each other, or even better, with some intermediary arbiter. The second question is regarding security. If I'm going to be running user code in my server, I want to ensure strict security conditions, like no file system access, no networking, etc. Otherwise it would be a safe heaven for hackers. I will be implementing the engine/arbiter in .NET. I would like to support at least C#, C++, Java and Python for the user's implementations. I'm willing to write interfaces for each of these languages to simplify the user interaction with the system. Thanks in advance.

    Read the article

  • Complexity of defense AI

    - by Fredrik Johansson
    I have a non-released game, and currently it's only possible to play with another human being. As the game rules are made up by me, I think it would be great if new players could learn basic game play by playing against an AI opponent. I mean it's not like Tennis, where the majority knows at least the fundamental rules. On the other hand, I'm a bit concerned that this AI implementation can be quite complex. I hope you can help me with an complexity estimation. I've tried to summarize the gameplay below. Is this defense AI very hard to do? Basic Defense Game Play Player Defender can move within his land, i.e. inside a random, non-convex, polygon. This land will also contain obstacles modeled as polygons, that Defender has to move around. Player Attacker has also a land, modeled as another such polygon. Assume that Defender shall defend against Attacker. Attacker will then throw a thingy towards Defender's land. To be rewarded, Attacker wants to hit Defender's land, and Defender will want to strike away the thingy from his land before it stops to prevent Attacker from scoring. To feint Defender, Attacker might run around within his land before the throw, and based on these attacker movements Defender shall then continuously move to the best defense position within his land.

    Read the article

  • Checker AI in visual basic not working [on hold]

    - by Eugene Galkine
    I am trying to a make checkers in visual basic with ai. I am using the minimax algorithm (or at least what I understand of it) and it works, except the ai is retarded and plays like it is trying to loose and I tried to switch around the min and the max but the results are IDENTICAL. I am pissed of and have been trying to fix it for over a week now, I would really appreciate it if someone could help me out here. I have 3 years experience of programming (in Java, only about of month of VB experience) and I always am able to solve all my errors on my own so I don't know why I can't get this to work. The program is not at all optimized or anything at this point and is over 1.2K lines long, so here is the entire vb project instead: https://www.dropbox.com/sh/evii0jendn93ir2/9fntwH2dNW I would really appreciate any help I could get.

    Read the article

  • Tips for building an AI for a 2D racing game

    - by declique
    I have a school project to build an AI for a 2D racing game in which it will compete with several other AIs (no collision). We are given a black and white bitmap image of the racing track, we are allowed to choose basic stats for our car (handling, acceleration, max speed and brakes) after we receive the map. The AI connects to the game's server and gives to it several times a second numbers for the current acceleration and steering. The language I chose is C++, by the way. The questions are: What is the best strategy or algorithm (since I want to try and win)? I currently have in mind some ideas found on the net and one or two of my own, but I would like before I start to code that my perspective is one of the best. What good books are there on that matter? What sites should I refer to?

    Read the article

  • Making an AI walk on a NavigationMesh (2D/Top-Down game)

    - by Lennard Fonteijn
    For some time I have been working on a framework which should make it possible to generate 2D levels based on a set of rules specified by level designers. You can read more about it here as I won't go into details: http://www.jorisdormans.nl/article.php?ref=engineering_emergence Anyway, I'm now at the point of putting the framework to use and have trouble coming up with a solution for AI. I decided to implement a NavigationMesh in the generated levels as I already have that information to start with. Consider the following image (borrowed from http://www.david-gouveia.com/pathfinding-on-a-2d-polygonal-map/): When I run A* on the NavigationMesh, the red path would be suggested when I want to go from point A to B (either direction). However, I don't want my AI to walk that path directly and clipping corners, I'd rather want them to follow the more logical black path. How would I go about going from the Red path to the Black path, are there any algorithms for this. Which steps do I take? Is A* the proper solution for this at all? For some additional information: The proof-of-concept game is a 2D top-down game written in C#, but examples/references in any language are welcome!

    Read the article

  • "Untangle"-Game AI

    - by M0rgenstern
    I am trying to program an AI for such untangle games like Untangle game. I tried the following possibilities: 1) Just set one node after the other to a random place. If every node was moved once, start over with the first node in the list. 2) First move all nodes which have the most wrong connections. If all were moved once, move the nodes which have the fewest (but not 0) wrong connections. If all were moved but there are some left, move all which are left. If none are left start over. 3) Just 2) bust starting with the nodes with the fewest connections. 4)/5) As 2) and 3) but when I didn't move nodes which have only correct connections. All of these approaches are too slow and inefficient. Can anyone suggest a solution which does not depend so much on fortune?

    Read the article

  • Obtaining In game Warcraft III data to program standalone AI

    - by Slav
    I am implementing common purpose behavioral algorithm and would like to test it under my lovely Warcraft III game and watch how it will fight against real players. The problem is how to obtain information about in game state (units, structures, environment, etc. ). Algorithm needs access to hard drive and possibly distributed computing, that's why JASS (WC3 Editor language) usage doesn't solve the issue. Direct 3D hooking is an approach, but it wasn't done for WC3 yet and significant drawback is inability to watch online at how AI performs since it uses the viewport to issue commands. How in game data can be obtained to a different process in a fastest and easiest way? Thank you.

    Read the article

  • Using Pragma in Oracle Package Body

    - by asalamon74
    I'd like to create an Oracle Package and two functions in it: A public function ( function_public ) and a private one ( function_private ). The public function calls the private one. I'd like to add the same pragma to the functions: WNDS, WNPS. Without the pragma I can create a code like this: CREATE OR REPLACE PACKAGE PRAGMA_TEST AS FUNCTION function_public(x IN VARCHAR2) RETURN VARCHAR2; END PRAGMA_TEST; CREATE OR REPLACE PACKAGE BODY PRAGMA_TEST AS FUNCTION function_private(y IN VARCHAR2) RETURN VARCHAR2 IS BEGIN -- code END; FUNCTION function_public(x IN VARCHAR2) RETURN VARCHAR2 IS BEGIN -- code -- here is a call for function_private -- code END; END PRAGMA_TEST; If I'd like to add WNDS, WNPS pragma to function_public I should also add the same pragma to function_private because function_public calls function_private. It seems to me pragma can be used only in the package declaration, and not in package body, so I have to declare function_private in the package as well: CREATE OR REPLACE PACKAGE PRAGMA_TEST AS FUNCTION function_private(y IN VARCHAR2) RETURN VARCHAR2; PRAGMA RESTRICT_REFERENCES( function_private, WNDS, WNPS); FUNCTION function_public(x IN VARCHAR2) RETURN VARCHAR2; PRAGMA RESTRICT_REFERENCES( function_public, WNDS, WNPS); END PRAGMA_TEST; CREATE OR REPLACE PACKAGE BODY PRAGMA_TEST AS FUNCTION function_private(y IN VARCHAR2) RETURN VARCHAR2 IS BEGIN -- code END; FUNCTION function_public(x IN VARCHAR2) RETURN VARCHAR2 IS BEGIN -- code -- here is a call for function_private -- code END; END PRAGMA_TEST; This solution makes my function_private public as well. Is there a solution to add pragma to a function which can be found only in the package body?

    Read the article

1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >