Opponent modeling in multi-agent game playing and decision making allows agents to recursively model their opponent, creating increasingly complex models of increasingly sophisticated opponents. Human participants show this ability to predict the actions of others through theory of mind, by explicitly attributing unobservable mental content such as beliefs, desires, and intentions to an opponen...

Negotiation is a challenging domain for virtual human research. One aspect of this problem, known as opponent modeling, is discovering what the other party wants from the negotiation. Research in automated negotiation has yielded a number opponent modeling techniques but we show that these methods do not easily transfer to human-agent settings. We propose a more effective heuristic for inferrin...

In competitive domains, the knowledge about the opponent can give players a clear advantage. This idea lead us in the past to propose an approach to acquire models of opponents, based only on the observation of their input-output behavior. If opponent outputs could be accessed directly, a model can be constructed by feeding a machine learning method with traces of the opponent. However, that is...

Although in theory opponent modeling can be useful in any adversarial domain, in practice it is both difficult to do accurately and to use effectively to improve game play. In this paper, we present an approach for online opponent modeling and illustrate how it can be used to improve offensive performance in the Rush 2008 football game. In football, team behaviors have an observable spatio-temp...

Opponent modeling is necessary in multi-agent settings where secondary agents with competing goals also adapt their strategies, yet it remains challenging because strategies interact with each other and change. Most previous work focuses on developing probabilistic models or parameterized strategies for specific applications. Inspired by the recent success of deep reinforcement learning, we pre...

Opponent modeling is a critical mechanism in repeated games. It allows a player to adapt its strategy in order to better respond to the presumed preferences of his opponents. We introduce a new modeling technique that adaptively balances exploitability and risk reduction. An opponent’s strategy is modeled with a set of possible strategies that contain the actual strategy with a high probability...

Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interactive strategy is a hard problem because it depends mostly on the behavior of the others. In this work, interaction among agents is represented as a repeated two-player game, where the agents’ objective is to look for a strategy that maximi...

Kriesgpiel, or partially observable chess, is appealing to the AI community due to its similarity to real-world applications in which a decision maker is not a lone agent changing the environment. This paper applies the framework of Interactive POMDPs to design a competent Kriegspiel player. The novel element, compared to the existing approaches, is to model the opponent as a competent player a...

We present an optimization technique to find hue constant RGB sensors. The hue representation is based on a log RGB opponent color space that is invariant to brightness and gamma. While modeling the visual response did not derive the opponent space, the hue definition is similar to the ones found in CIE Lab and IPT. Finding hue constant RGB sensors through this optimization might be applicable ...

This paper describes how BDI modeling can be exploited in the design of software agents that support naval training sessions. The architecture, specifications, and embedding of the BDI agent in a simulation environment are described. Subsequently, the agent’s functioning was evaluated in complex, real life, training situations for naval officers.