Multi-opponent James functions

Opponent Modeling in Multi-Agent Systems

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...

Multi-robot team response to a multi-robot opponent team

Adversarial multi-robot problems, where teams of robots compete with one another, require the development of approaches that span all levels of control and integrate algorithms ranging from low-level robot motion control, through to planning, opponent modeling, and multiagent learning. Small-size robot soccer, a league within the RoboCup initiative, is a prime example of this multi-robot team a...

Feature-Based Declarative Opponent-Modelling in Multi-Agent Systems

Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning1

In bilateral negotiation, two parties aim at reaching a joint agreement. They do so by exchanging various offers or bids using e.g. an alternating offers protocol [2]. In reaching such an agreement both parties usually aim to satisfy their own interests as best as possible, but have to take their opponent’s preferences into account as well to reach an agreement at all. This is complicated by th...

Opponent modelling in automated multi-issue negotiation using Bayesian learning

The efficiency of automated multi-issue negotiation depends on the availability and quality of knowledge about an opponent. We present a generic framework based on Bayesian learning to learn an opponent model, i.e. the issue preferences as well as the issue priorities of an opponent. The algorithm proposed is able to effectively learn opponent preferences from bid exchanges by making some assum...