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moeen k

good field in game AI research

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hi.

im a master of science student on AI and im searching for a good field and subject to work on.

 

there are many suggestions like working on recommander systems and machine learning a using them for predictors and...

 

as my most interested field is videogames im searching for a good subject to work on. a subject that is less worked on and has a good use in gaming industry. for example there is a subject that i read for adaptive game AI based on player ability and behavior or learning game AI that improves from best players behavior and statistics and.. but these subjects has some problems. first there are many papers for them and they are used less in game industry as in game AI learning AIs get better fast and make the game hard to play and in campaigns the AI just does a fixed scenrio for the story.

 

can you give me better view and information? thank you for helping

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I don't know the field of AI, but from a science point of view, problems is good.

Master of science is supposed to push the border a bit. Even a careful analysis of the approaches and its problems would be valuable. (Even more valuable than you may think, overview papers are a lot of work to make, but unlock an area for many quickly.)

Of course it depends on the kind of work you want to do, more an analysis kind of job, or more an experimental kind of work.
(The latter typically means a quick inventory, then dive in some sub-aspect.)

In all cases, you probably want to discuss things with your supervisor.

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in game AI, if you want to win the nobel prize, come up with a faster and easier A*.

 

some devs favor planners.

 

some favor HFSMs.

 

some favor scripted rule based behavior.

 

i'm partial to modular hierarchies of expert systems myself, where each "module" is an "expert" at some type of decision making in a behavior tree (i guess you would say). a module may be a behavior tree, a FSM, a planner, NN, whatever works best for that bit of the overall system.

 

i've considered all types of AI over the years, but have never found one superior to the hierarchical "expert system" using appropriate AI types. although expert system really describes a behavior, its not a particular implementation type, such as decision tree, although decision trees are a common means of implementing them.

 

so as you can see, the intersection of popular academic AI topics and game AI is almost the empty set.

 

we get someone on here about every six moths asking this type of question.

 

most are surprised to learn that game AI and academic AI research are so far apart. and FYI, I completed the software engineering program at OSU, and i took classes in AI.

 

i think there are four realities in game AI that one doesn't usually have to consider in academia, which drives the difference in the evolution of AI approaches:

1. realtime - you get 5ms to do all AI for 1000 entities.

2. kiss - keep it stupid simple

3. gotta ship - the design must implemented, debugged, and working correctly in less time than it really take to thoroughly test all cases and combos, yet all must work. 

4. underpowered hardware - you must design for the most abysmally under powered processor the game can possibly run on. i have a saying: "put a Cray on every users desk, and i'll build you a REAL game".

 

another one might be:

5. candy sells - at the 11th hour, your millisec budget is cut in half, because render is slow. now you must update those 1000 entities in 2.5ms, not 5ms - yet their behavior can't be adversely affected.

Edited by Norman Barrows

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A suggested project

 

Analysing what is happening in a played game - either by output analysis (graphics, io events) or hooking into the guts of the program (few published games have a 'program interface' available though).

 

Observing and classifying activities going on (both the environment and what the player(s) do in reaction to it).

 

Interpreting, Classification and Factoring (to build an internal symbolic model)

 

This is a basic 'tool' of anything further in AI - converting and boiling down everything into machine useable data -- and then reexpressing it in a human readable form (needed to verify the whole thing works and THEN to be made use by  the human component of AI - where the human is asked to give hints/teach about what is good or bad - which always has to be done one way or another)

 

Game companies want to see how players react to their game (during development) to spot patterns which indicate it is too simple or overcomplicated or too narrow in solution/activities done by real players  (having a player use just one or two optimal tactics the whole game makes it a waste to develop a whole lot of possibilities and make the game overly repetitive/less interesting.

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and one more question:

 

i chose a machine learning system that bots learn from best players to make their behave and performance better.  what are most important multiplayer games with open source script ?

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and one more question:

 

i chose a machine learning system that bots learn from best players to make their behave and performance better.  what are most important multiplayer games with open source script ?

 

You realize that there are probably 3000 academics working on this exact thing (with fairly tepid success rates), don't you?

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You realize that there are probably 3000 academics working on this exact thing (with fairly tepid success rates), don't you?

 

yes. i know but i think its the most intereting thing in many papers i read.

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