Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

This paper presents a large-scale application of multiagent evolutionary modeling to the
proposed new electricity trading arrangements (NETA) in the UK. This is a detailed plant-by …

Learning and intelligence are intimately related to each other. It is usually agreed that a
system capable of learning deserves to be called intelligent; and conversely, a system being …

Agents can achieve effective interaction with previously unknown other agents by
maintaining beliefs over a set of hypothetical behaviours, or types, that these agents may …

This paper describesthe user modeling approach applied in I-Help, a distributed multi-agent
based collaborative environment for peer help. There is a multitude of user modeling …

We adopt the decision-theoretic principle of expected utility maximization as a paradigm for
designing autonomous rational agents, and present a framework that uses this paradigm to …

Adaptation to other initially unknown agents often requires computing an effective counter-
strategy. In the Bayesian paradigm, one must find a good counter-strategy to the inferred …

We adopt decision theory as a descriptive paradigm to model rational agents. We use
influence diagrams as a modeling representation of agents, which is used to interact with …

An agent that interacts with other agents in multi-agent systems can benefit significantly from
adapting to the others. When performing active learning, every agent's action affects the …