Potential Applications of Opponent-Model Search1

Iida, H.; Uiterwijk, Jos W. H. M.; Herschberg, I.S.; Vandenherik, Hj

doi:10.3233/icg-1994-17103

Cited by 19 publications

(27 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most previous techniques focused on a particular model of interaction, such as repeated games [9-11, 13, 31] two-player, zero-sum, perfect information games [6,12,21,23,37,41] imperfect information games [4] , and market systems [40,47]. Stone et al [44] and Bruce et al [5] applied Opponent Modelling techniques to a robotic soccer game, which is a dynamic environment with continuous action space and incomplete information.…”

Section: Related Workmentioning

confidence: 99%

“…The particular selection of such a scheme can potentially limit the types of opponent strategies that can be handled by the modelling agent. For instance, opponents assumed to be using regular strategies [21] represent their opponent strategies as utility functions, while Carmel and Markovitch [7] and Gao et al [14] represent strategies as pairs consisting of the utility function and search depth. Schapire et al [40] do not represent the opponent strategy explicitly, but include features indicating the opponents' identities in order to learn their effect on predicted prices.…”

Section: Related Workmentioning

confidence: 99%

“…-Risk: The model is ultimately used to anticipate the opponent agent's decisions, or to simulate its actions. If, however, the model is not entirely accurate, then relying on its predictions may harm the agent's performance rather than improving it [22]. Note that even in the unlikely event that the agent possesses an exact model of its opponent, utilizing it will not guarantee an exact prediction due to the limited simulation resources available during a real interaction.…”

Section: Introductionmentioning

confidence: 95%

See 2 more Smart Citations

Learning and Exploiting Relative Weaknesses of Opponent Agents

Markovitch

Reger

2005

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of such adaptation is Opponent Modeling, in which a model of an opponent is acquired and utilized as part of the agent's decision procedure in future interactions with this opponent. However, acquiring an accurate model of a complex opponent strategy may be computationally infeasible. In addition, if the learned model is not accurate, then using it to predict the opponent's actions may potentially harm the agent's strategy rather than improving it. We thus define the concept of opponent weakness, and present a method for learning a model of this simpler concept. We analyze examples of past behavior of an opponent in a particular domain, judging its actions using a trusted judge. We then infer a weakness model based on the opponent's actions relative to the domain state, and incorporate this model into our agent's decision procedure. We also make use of a similar self-weakness model, allowing the agent to prefer states in which the opponent is weak and our agent strong; where we have a relative advantage over the opponent. Experimental results spanning two different test domains demonstrate the agents' improved performance when making use of the weakness models.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

See 1 more Smart Citation

Learning and Exploiting Relative Weaknesses of Opponent Agents

Markovitch

Reger

2005

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…This approach falls under the general framework of opponent modeling search (Carmel & Markovitch, 1996a;Iida et al, 1993;Iida et al, 1994, Donkers, 2003. There, the action of the opponent is predicted by simulating its decision process using an opponent model (typically a minimax algorithm using a given or learned evaluation function).…”

Section: Decision Nodes For Other Agentsmentioning

confidence: 99%

“…PIDM is based on the simulation approach, where models of other agents' strategies are used to simulate their decision making in order to predict their actions (Iida et al, 1993;Iida et al, 1994;Donkers et al, 2001;Donkers, 2003;Carmel & Markovitch, 1998;Carmel and Markovitch, 1996a). PIDM extends the previous works by allowing any combination of opp-agents and co-agents and by combining search with model-based Monte Carlo sampling for partially observable states.…”

Section: Bridge Biddingmentioning

confidence: 99%

Learning to bid in bridge

Amit

Markovitch

2006

Mach Learn

View full text Add to dashboard Cite

Bridge bidding is considered to be one of the most difficult problems for gameplaying programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm.

show abstract

Current Challenges in Multi-player Game Search

Sturtevant

2006

Computers and Games

View full text Add to dashboard Cite

Years of work have gone into algorithms and optimizations for twoplayer perfect-information games such as Chess and Checkers. It is only more recently that serious research has gone into games with imperfect information, such as Bridge, or game with more than two players or teams of players, such as Poker. This work focuses on multi-player game search in the card games Hearts and Spades, providing an overview of past research in multi-player game search and then presents new research results regarding the optimality of current search techniques and the need for good opponent modeling in multi-player game search. We show that we are already achieving near-optimal pruning in the games Hearts and Spades.

show abstract

Potential Applications of Opponent-Model Search1

Cited by 19 publications

References 0 publications

Learning and Exploiting Relative Weaknesses of Opponent Agents

Learning and Exploiting Relative Weaknesses of Opponent Agents

Learning to bid in bridge

Current Challenges in Multi-player Game Search

Contact Info

Product

Resources

About