We present methods to estimate perceptual uniformity of color spaces and to derive a perceptually uniform RGB space using geometrical criteria defined in a logarithmic opponent color representation.

Modeling and reasoning about an opponent in a competitive environment is a difficult task. This paper uses a reinforcement learning framework to build an adaptable agent for the game of 1-card poker. The resulting agent is evaluated against various opponents and is shown to be very competitive.

When a negotiating agent is presented with an offer by its opponent, it is faced with a decision: it can accept the offer that is currently on the table, or it can reject it and continue the negotiation. Both options involve an inherent risk: continuing the negotiation carries the risk of forgoing a possibly optimal offer, whereas accepting runs the risk of missing out on an even better future ...

Stochastic Opponent Modeling Agents (SOMA) have been proposed as a paradigm for reasoning about cultural groups, terror groups, and other socio-economic-political-military organizations worldwide. In this paper, we describe a case study that shows how SOMA was used to model the behavior of the terrorist organization, Hamas. Our team, consisting of a mix of computer scientists, policy experts, a...

One drawback with using plan recognition in adversarial games is that often players must commit to a plan before it is possible to infer the opponent’s intentions. In such cases, it is valuable to couple plan recognition with plan repair, particularly in multi-agent domains where complete replanning is not computationally feasible. This paper presents a method for learning plan repair policies ...

Most state of the art learning algorithms do not fare well with agents (computer or humans) that change their behaviour in time. This is the case because they usually do not model the other agents’ behaviour and instead make some assumptions that for real scenarios are too restrictive. Furthermore, considering that many applications demand different types of agents to work together this should ...

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 its opponents. We introduce a modeling technique that adaptively balances safety and exploitability. The opponent’s strategy is modeled with a set of possible strategies that contains the actual one with high probability. The algorithm i...

The Theory of Mind provides a framework for an agent to predict the actions of adversaries by building an abstract model of their strategies using recursive nested beliefs. In this paper, we extend a recently introduced technique for opponent modelling based on Theory of Mind reasoning. Our extended multi-agent Theory of Mind model explicitly considers multiple opponents simultaneously. We intr...

As adversarial environments become more complex, it is increasingly crucial for agents to exploit the mistakes of weaker opponents, particularly in the context of winning tournaments and competitions. In this work, we present a simple post processing technique, which we call Perfect Information Post-Mortem Analysis (PIPMA), that can quickly assess the playing strength of an opponent in certain ...

Stochastic Opponent Modeling Agents (SOMA) have been proposed as a paradigm for reasoning about cultural groups, terror groups, and other socioeconomic-political-military organizations worldwide. In this paper, we describe a case study that shows how SOMA was used to model the behavior of the terrorist organization, Hezbollah. Our team, consisting of a mix of computer scientists, policy experts...