Introduction Agents operate and exist in some environment, which typically is both computational and physical. The environment might be open or closed, and it might or might not contain other agents. Although there are situations where an agent can operate usefully by itself, the increasing interconnection and networking of computers is making such situations rare, and in the usual state of affairs the agent interacts with other agents. Whereas the previous chapter defined the structure and characteristics of an individual agent, the focus of this chapter is on systems with multiple agents. At times, the number of agents may be too numerous to deal with them individually, and it is then more convenient to deal with them collectively, as a society of agents. In this chapter, we will learn how to analyze, describe, and design environments in which agents can operate effectively and interact with each other productively. The environments will provide a computational infrastructu

Automation of planning techniques can potentially save a great deal of design and programming time, and can help robots design plans when human help is not available. Currently, the computational cost of machine planning algorithms prevents wide-spread use of these systems, and these costs are magnified in multiagent systems. The most expensive and time-consuming aspect of multiagent systems, communication, must be reduced if multiagent planning is to be practical. We propose a method by which agents may reduce both planning and communication costs by planning with stringent social laws, relaxing the laws as needed to find a solution. We provide and implement a practical model for representing and relaxing social laws, and show a method for learning laws in minimal time; we also present a method for generating and relaxing exclusive resource allocations in specific planning situations, and show that the method performs significantly better than random or no allocation in the average ca...