memory and analogy in game-playing agents jonathan rubin & ian watson university of auckland...
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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
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Overview➲ Introduction
➲ General Game Playing
➲ Lazy Learners
➲ Memory in game-playing agents
➲ Analogical Reasoning
➲ Analogical Knowledge Transfer in GGP
➲ Conclusion
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Introduction
➲ Views and ideas about a possible approach to general game playing using memory and analogy
➲ Possible research direction
➲ Suggestions and feedback welcome
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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
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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
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Approaches to General Game Playing
➲ Conditional Planning (One-player games)
➲ Automatic Programming – automatic generation of programs that achieve specified objectives
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General Game PlayingOpportunities
➲ Learning
Playing multiple instances of a single game
Playing multiple games against a single player
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General Game PlayingOpportunities
➲ Identifying common lessons that can be transferred from one game instance to another
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Possible Approach toGeneral Game Playing
➲ Lazy learning approach
➲ Record a memory of experiences
➲ Analogical reasoning to generalize beyond game domains
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Lazy Learners
➲ Lazy Learners Defer processing of their inputs until they
receive requests for information (Aha,
1997)
Use local approaches
Ability to generalize well
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Memory in Games
➲ One possible definition:
Any persistent knowledge an agent has that it does not need to deduce algorithmically
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Memory-based Agents
➲ GINA – Othello (De Jong & Schultz, 1988)
➲ CHEBR – Checkers (Powell et. al., 2004)
➲ Chess (Sinclair, 1998)
➲ Casper – Poker (Rubin & Watson, 2007)
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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
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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?
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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
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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
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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
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Analogical Knowledge Transfer
Higher value cards in Poker are stronger than lower value cards
Higher value property is also better in Monopoly
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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
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Analogical Knowledge Transfer
In poker you must spend money to win money
knowledgeknowledge
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Knowledge Transfer
Superficially there is nothing in common between Poker & Monopoly
Knowledge is (in theory) transferable between the games
knowledgeknowledge
?
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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
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