Handbook of Knowledge Representation Edited Multi-agent Systems 24.1 Introduction

We review the state of the art in knowledge representation formalisms for multi-agent systems. We divide work in this area into two categories. In the first category are approaches that attempt to represent the cognitive state of rational agents, and to characterize logically how such a state leads a rational agent to act. We begin by motivating this approach. We then describe four of the best-known such logical frameworks, and discuss the possible roles that such logics can play in helping us to engineer artificial agents. In the second category are approaches based on representing the strategic structure of a multi-agent environment, and in particular, the powers that agents have, either individually or in coalitions. Here, we describe Coalition Logic, Alternating-time Temporal Logic (ATL), and epistemic extensions. The discipline of knowledge representation focuses on how to represent and reason about environments with various different properties, usually with the goal of making decisions, for example about how best to act in this environment. But what are the things that are actually doing this representation and reasoning? The now-conventional terminology is to refer to these entities as agents. The agents may be computer programs (in which case they are called software agents) or they may be people like you or I. The case where there is only assumed to be one agent in the environment (for example, a single autonomous robot operating in a warehouse) is usually the simplest scenario for knowledge representation, and often does not require techniques beyond those described elsewhere in this book. However, where there are multiple agents in the environment, things get much more interesting—and challenging. This is because it becomes necessary for an agent to represent and reason about the other agents in the environment. Again there are two possibilities. The first is that all the agents in the environment can be assumed to share a common purpose. This might be the case, for example, if we are designing a multi-robot system to operate in a warehouse environment. Here, we can assume the robots share a common purpose because we can design them that way. However, the second case is again much more interesting, and