In this paper, we show ACT-R agents capable of metacognitive reasoning about opponents in the repeated prisoner’s dilemma. Two types of metacognitive agent were developed and compared to a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent (opponent-perspective) takes the perspective of the opponent to anticipate the opponent’s future actions and respon...

In this paper, we show ACT-R agents capable of metacognitive reasoning about opponents in the repeated prisoner’s dilemma. Two types of metacognitive agent were developed and compared to a non-metacognitive agent and two fixed-strategy agents. The first type of metacognitive agent (opponent-perspective) takes the perspective of the opponent to anticipate the opponent’s future actions and respon...

We propose an opponent modeling approach for nolimit Texas hold-em poker that starts from a (learned) prior, i.e., general expectations about opponent behavior and learns a relational regression tree-function that adapts these priors to specific opponents. An important asset is that this approach can learn from incomplete information (i.e. without knowing all players’ hands in training games).

Utilizing resources and research from the University of Alberta Poker research group, we are investigating opponent modeling improvements. Currently, our simple poker bot plays online against instantiations of PokiBots, the poker machine created by the University of Alberta research group. After some decision rule building, our poker bot is competitive. Our next step is to build upon this resea...

In a team-based multiagent system, the ability to construct a model of an opponent team’s joint behavior can be useful for determining an agent’s expected distribution over future world states, and thus can inform its planning of future actions. This paper presents an approach to team opponent modeling in the context of the RoboCup simulation coach competition. Specifically, it introduces an au...

Video games are quickly becoming a significant part of society with a growing industry that employs a wide range of talent, from programmers to graphic artists. Video games are also becoming an interesting and useful testbed for Artificial Intelligence research. Complex, realistic environmental constraints, as well as performance considerations demand highly efficient AI techniques. At the same...

Poker is an interesting test-bed for artificial intelligence research. It is a game of imperfect knowledge, where multiple competing agents must deal with risk management, opponent modeling, unreliable information, and deception, much like decision-making applications in the real world. Opponent modeling is one of the most difficult problems in decision-making applications and in poker it is es...

Multi-agent systems are broadly known for being able to simulate real-life situations which require the interaction and cooperation of individuals. Opponent modeling can be used along with multi-agent systems to model complex situations such as competitions like soccer games. In this paper, a model for predicting opponent moves is presented. The model is based around an offline step (learning p...

An issue with learning effective policies in multi-agent adversarial games is that the size of the search space can be prohibitively large when the actions of both teammates and opponents are considered simultaneously. Opponent modeling, predicting an opponent’s actions in advance of execution, is one approach for selecting actions in adversarial settings, but it is often performed in an ad hoc...