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Automated Planning in Repeated Adversarial Games

Automated Planning in Repeated Adversarial Games
Automated Planning in Repeated Adversarial Games
Game theory's prescriptive power typically relies on full rationality and/or self-play interactions. In contrast, this work sets aside these fundamental premises and focuses instead on heterogeneous autonomous interactions between two or more agents. Specifically, we introduce a new and concise representation for repeated adversarial (constant-sum) games that highlight the necessary features that enable an automated planing agent to reason about how to score above the game's Nash equilibrium, when facing heterogeneous adversaries. To this end, we present TeamUP, a model-based RL algorithm designed for learning and planning such an abstraction. In essence, it is somewhat similar to R-max with a cleverly engineered reward shaping that treats exploration as an adversarial optimization problem. In practice, it attempts to find an ally with which to tacitly collude (in more than two-player games) and then collaborates on a joint plan of actions that can consistently score a high utility in adversarial repeated games. We use the inaugural Lemonade Stand Game Tournament to demonstrate the effectiveness of our approach, and find that TeamUP is the best performing agent, demoting the Tournament's actual winning strategy into second place. In our experimental analysis, we show hat our strategy successfully and consistently builds collaborations with many different heterogeneous (and sometimes very sophisticated) adversaries.
376-383
Munoz de Cote, Enrique
0b38ed33-005a-44e5-aa5d-cae0474039ae
Chapman, Archie
2eac6920-2aff-49ab-8d8e-a0ea3e39ba60
Sykulski, Adam M.
6cec63f1-86f7-435f-8192-cc1fe10d9fad
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Munoz de Cote, Enrique
0b38ed33-005a-44e5-aa5d-cae0474039ae
Chapman, Archie
2eac6920-2aff-49ab-8d8e-a0ea3e39ba60
Sykulski, Adam M.
6cec63f1-86f7-435f-8192-cc1fe10d9fad
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Munoz de Cote, Enrique, Chapman, Archie, Sykulski, Adam M. and Jennings, Nick (2010) Automated Planning in Repeated Adversarial Games. 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), United States. 08 - 11 Jul 2010. pp. 376-383 .

Record type: Conference or Workshop Item (Other)

Abstract

Game theory's prescriptive power typically relies on full rationality and/or self-play interactions. In contrast, this work sets aside these fundamental premises and focuses instead on heterogeneous autonomous interactions between two or more agents. Specifically, we introduce a new and concise representation for repeated adversarial (constant-sum) games that highlight the necessary features that enable an automated planing agent to reason about how to score above the game's Nash equilibrium, when facing heterogeneous adversaries. To this end, we present TeamUP, a model-based RL algorithm designed for learning and planning such an abstraction. In essence, it is somewhat similar to R-max with a cleverly engineered reward shaping that treats exploration as an adversarial optimization problem. In practice, it attempts to find an ally with which to tacitly collude (in more than two-player games) and then collaborates on a joint plan of actions that can consistently score a high utility in adversarial repeated games. We use the inaugural Lemonade Stand Game Tournament to demonstrate the effectiveness of our approach, and find that TeamUP is the best performing agent, demoting the Tournament's actual winning strategy into second place. In our experimental analysis, we show hat our strategy successfully and consistently builds collaborations with many different heterogeneous (and sometimes very sophisticated) adversaries.

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More information

Published date: July 2010
Additional Information: Event Dates: 8-11 July, 2010
Venue - Dates: 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), United States, 2010-07-08 - 2010-07-11
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 271306
URI: http://eprints.soton.ac.uk/id/eprint/271306
PURE UUID: 62ee5f38-5da2-4209-8e50-38685bce1ba7

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Date deposited: 25 Jun 2010 10:12
Last modified: 19 Jul 2019 22:13

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