PLAYING GAMES WITH COGNITIVE COMPUTING

Cognitive Systems Institute Group Speaker Series

Simon Ellis

Department of Computer Science ◇ Tetherless World Constellation Rensselaer Polytechnic Institute, Troy, NY 12180

ELLISS5@RPI.EDU

Thursday, 30th April, 2015

Q10

v  Lots of games!…

v  AI agents play some games well and some badly, but why?

Games

Q10 Game complexity

v  Arises from multiple aspects of the game design; e.g.:v  Data structure (amount of game state information)

v  Rule structure and complexity

v  Level of bluffing and inference

v  Degree of openness

v  …

v  Game theory defines other parameters for games; e.g.:v  Zero-/Non zero-sum

v  Deterministic/Stochastic

v  Impartial/Partisan

v  Perfect/Imperfect information

Q10 Game-playing in A.I.

Q10 IBM Watson

v  Designed to play – and win – at a “humans-only” game

v  Consider the search space of Jeopardy!:v  English language (including borrows, loan words, calques…)

v  Proper nouns

v  Foreign words

v  Phrases

v  …

v  How did Watson manage it – with 3 seconds per question?

Q10 IBM Watson

v  Serious hardware (~2,800 IBM Power7 cores)

v  More importantly…

Q10 “Cognitive game-playing”

v  Drawing inspiration from two sourcesv  DeepQA (Watson) architecture

v  Human approaches to playing games

v  How do humans play games?v  Questions (“Where can I play?”, “What if I/they play/move…?”)

v  Intuition or instinct based on past play experience

v  Logic (inductive, deductive, abductive or analogical)

v  Mood

v  Strategy

v  Self-evaluation (“Am I winning?”, “Should I change strategy?”…)

v  …

Q10 Architecture model

v  Architecture…v  … was inspired by the design of the DeepQA pipeline

v  … is informed by consideration of how people play games

v  … uses numerous tools (“evaluators”) to judge game stateu  Evaluators correspond to the sections and subsections of the pipeline

PRIMARY GENERAL ANALYSIS

Where can I play? Where can I not play?

What can I play?

SECONDARY GENERAL ANALYSIS

What is my score? Can I win this turn?

Do I have any valuable tiles? What is my position like?

MOVE GENERATION

What moves exist? Do chains of moves exist?

PRIMARY MOVE SCORING

Will this advance my position? What would my new score(s) be?

GENERAL META-ANALYSIS

Who is winning? What tile might come up next? Can I disrupt a player’s game? What happens if I play tile M?

INPUT STATE

OUTPUT STATE

TACTICS

Can I control more of the board? How many tiles can I play now?

Can I swap hands? Should I do so? Should I retain tile Q for later?

TILE-SPECIFIC META-ANALYSIS

How can I use tile X best? Does tile Y give me any benefit? Can I perform combo move Z?

FINAL SCORING AND RANKING

Which move has the highest score? What other moves score highly?

Which move gives me the highest score?

“DEEP THINKING”

How well does this move fit my tactics? Should I change my gameplay?

Is it worth playing a lesser move now?

Q10 “Deep thinking”

v  Could also be termed described as “meta-reasoning”v  Reasoning over meta-data

v  Meta-data come from various sourcesu  Data derived from information about the game (current & past states)

u  Self-analysis of agent’s performance

u  …

v  “Deep thinking” in strategyv  Agent has some pre-programmed “strategies”

v  Analysis of agent’s own performance using one of these strategies can be analysed (i.e. the agent has a degree of reflection)

v  The agent can decide to change its strategy if it determines it would be advantageous to do so

Q10 Proof of concept

v  To demonstrate the system, we need a complex game

v  Infinite City

v  Tile-based strategy gamev  Zero-sum, deterministic, sequential, finite, partisan, unstable,

combinatorial game of imperfect information for 2 to 6 players

v  Objectv  To obtain the highest score by controlling the largest area of the

‘infinite city’

Infinite City by Brent Keith. © 2009 by Alderac Entertainment Group. All rights reserved.

Q10 Infinite City

v  Basic gameplayv  Game starts with 5 tiles on the board face down

v  Each player gets 15 tokens and a hand of 5 tiles

v  Player places a tile where permitted and claims it with a token

v  Each tile has instructions which must be followed

v  At end of game, the player with the highest score wins

Infinite City by Brent Keith. © 2009 by Alderac Entertainment Group. All rights reserved.

Q10 “Cognitive game-playing”

v  Development of evaluatorsv  Mostly for gameplay decisions

v  Some are already conceptualisedu  “Where can I play?”, “What can I play?”

u  “What is player X’s score?”, “What is the likelihood of drawing tile Y?”

v  Many others will be requiredu  Some will emerge during development; i.e. to do P, Q, R and S are needed

u  Others have emerged during research involving human players (e.g. RPI Games Club, undergraduate volunteers)

v  What about more general strategy?v  Without branch-and-bound, how can we make sure the agent plays

its best?

Q10 “Deep thinking”

v  Strategy is a major component in an AI for a complex gamev  Definition of strategy: “an overall methodology for playing a game”

v  Simple strategies will be developedv  Goal will generally be to maximise score

v  Several different methodologies possible in Infinite Cityu  Get single largest block of tiles

u  Get highest number of scoring tiles

u  Acquire score through tile bonus points

u  …

Q10 “Deep thinking”

v  “Deep thinking” system will evaluate performancev  Perform analysis over a set of evaluators

u  Which evaluators work well or badly will be a matter of research

u  Different strategies may well have some different inputs

v  Heuristics will be necessarily simpleu  May be as simple as a set of if (...) statements

u  Again, a matter of research to see what works well

v  Based on results, the agent may change its strategy

v  Aim is to provide the agent with a degree of self-reflectionv  Ability to judge its own performance using provided criteria

Q10 Conclusion

v  Watson demonstrated the efficacy of ‘cognitive computing’

v  “Cognitive game-playing” is a development of this techniquev  Many tabletop games have extremely large search spaces

v  Traditional A.I. search techniques do not work well for such games

v  This is a powerful and flexible approach to game-playingv  Provides a solution to problems of extreme game complexity

v  Self-analysis injected into system through “deep thinking”

v  Makes possible very powerful, flexible, interesting artificial gamersu  … which might, one day, take on the ultimate gaming challenge…

Q10

Acknowledgements I would like to thank my supervisor, Professor Jim Hendler, for his continued support and advice, and for taking a chance on a stranger with some crazy ideas and offering me the initial

opportunity to work with Watson. I would also like to thank Dr Chris Welty and Dr Siddharth Patwardhan for their assistance and insights which led semi-directly to this work, Dr Bijan Parsia (University of Manchester, UK) for his timely intervention in asking difficult questions which I had been avoiding, and Professor Selmer Bringsjord (RPI) for his consistently insightful comments

and observations. Additionally, sincere thanks are due to Dr Jonathan Dordic and Mr John Kolb (RPI) for their support, and to my other friends and colleagues at RPI likewise for theirs.