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Memory and Analogy in Game-Playing Agents
Jonathan Rubin & Ian Watson
University of Auckland Game AI Grouphttp://www.cs.auckland.ac.nz/research/gameai
Overview➲ Introduction
➲ General Game Playing
➲ Lazy Learners
➲ Memory in game-playing agents
➲ Analogical Reasoning
➲ Analogical Knowledge Transfer in GGP
➲ Conclusion
Introduction
➲ Views and ideas about a possible approach to general game playing using memory and analogy
➲ Possible research direction
➲ Suggestions and feedback welcome
General Game Playing
➲ Unlike specialized game players such as Deep Blue
➲ Able to play different games Accept the rules of the game
Play the game effectively without human
intervention
Approaches to General Game Playing
➲ Partial game tree search with automated evaluation functions
➲ Approximating the minimax value by computing an exact value via simplifying abstractions of the original game
Approaches to General Game Playing
➲ Conditional Planning (One-player games)
➲ Automatic Programming – automatic generation of programs that achieve specified objectives
General Game PlayingOpportunities
➲ Learning
Playing multiple instances of a single game
Playing multiple games against a single player
General Game PlayingOpportunities
➲ Identifying common lessons that can be transferred from one game instance to another
Possible Approach toGeneral Game Playing
➲ Lazy learning approach
➲ Record a memory of experiences
➲ Analogical reasoning to generalize beyond game domains
Lazy Learners
➲ Lazy Learners Defer processing of their inputs until they
receive requests for information (Aha,
1997)
Use local approaches
Ability to generalize well
Memory in Games
➲ One possible definition:
Any persistent knowledge an agent has that it does not need to deduce algorithmically
Memory-based Agents
➲ GINA – Othello (De Jong & Schultz, 1988)
➲ CHEBR – Checkers (Powell et. al., 2004)
➲ Chess (Sinclair, 1998)
➲ Casper – Poker (Rubin & Watson, 2007)
Benefits of Memory
➲ Memory can be used to augment other approaches
Informed pruning of game tree search –
Sinclair, GINA
➲ Or, approach can be entirely based on memory alone
Casper
CHEBR
Experience-based, Lazy learners
➲ The use of memory has been shown to be successful in a range of specialized game domains.
(Non)-Deterministic, (Im)perfect Information
➲ Lazy Learners are able to adapt well to new situations
➲ How can we extrapolate experience-based, lazy learners to handle multiple game domains?
Analogical Knowledge Transfer
Our expertise is in PokerLet’s consider how our Poker cases could be used in an unknown game, e.g., “Monopoly”
knowledgeknowledge
Analogical Knowledge Transfer
Poker cases have only three possible actions - Fold, Call & RaiseThese actions are useless in MonopolyBut they do provide a measure of how good or strong a Poker hand is: Fold = weak Call = OK Raise = strong
Analogical Knowledge Transfer
A pair (two of a kind) is the most basic Poker hand
Three of a kind is stronger
Obtaining all the properties of the same colour is good in Monopoly
Analogical Knowledge Transfer
Higher value cards in Poker are stronger than lower value cards
Higher value property is also better in Monopoly
Analogical Knowledge Transfer
A straight in Poker is a good hand
A continuous block of properties in Monopoly increases the chances of an opponent landing on you
Analogical Knowledge Transfer
In poker you must spend money to win money
knowledgeknowledge
Knowledge Transfer
Superficially there is nothing in common between Poker & Monopoly
Knowledge is (in theory) transferable between the games
knowledgeknowledge
?
Conclusion
➲ In the context of General Game playing➲ A memory-based (case-based) component
may sometimes be useful➲ Games of similar types (card, board, ...)
share concepts in common➲ Should be easier to transfer knowledge between them
➲ We believe it’s also possible to transfer knowledge between games of different types
ThanksWe really want community feedback on this