Socially Rational Agents . . .

Rationality relates to making the right decisions and producing successful behaviour. The notion of building rational agents is one of the main aims of research into artificial intelligence. The current predominant approach is to develop agents which follow a decision theoretic notion of rationality in which the agent maximizes the expected utility of its actions. Although this is intuitively and formally appealing, it lacks applicability in real systems where the agent is faced with resource limitations. Furthermore, this view fails to adequately cope with the case in which an agent is embedded within a system of interacting agents. In such an environment, the agent has the further consideration of the effects of its actions on other agents and the effect of their actions on itself. Therefore to operate effectively in such environments the agents need a principle of social rationality. In this paper we outline our preliminary thoughts on devising such a principle and indicate how it helps the agent strike a balance between its individual needs and the needs of the overall system. 1. Theories of rationality Rationality is all about doing the ‘right’ thing, where right equates to performing successful actions [1]. Agent rationality is concerned with intelligent decision making as defined by the mapping of percepts into actions. Thus determining whether an agent is behaving rationally can only be ascertained by examining the information that it possesses and by evaluating the success of the actions that it performs based upon this information. The predominant theory of rational decision making in agents is that of the economic principle of maximizing the expected gain of actions [2]. Decision theoretic rationality dictates that the agent should choose an action which will maximize the expected utility of performing that action given the probability of reaching a desired state in the world and the desirability of that state. However, this theory makes the assumption that the agent has both complete information and sufficient time to carry out the necessary reasoning. In reality, however, agents have limitations on their deliberation with regard to the resources they have available. Hence theories of decision making need to take such boundedness into consideration if the agents are to be applied in real systems. Recognising this shortcoming, work on bounded rationality [3], [4], [5] takes into consideration the fact that the agent is faced with resource limitations which affect its reasoning. Despite numerous attempts at overcoming the problems of resource bounds on reasoning, there is yet to emerge a definitive concept of bounded rationality which can be applied in real systems. In addition, most work on ideal and bounded rationality fails to recognize the importance of the fact that many systems are composed of several interacting agents and that the decisions that each agent makes have consequences on the others within the system. To this end, this work proposes an alternative view of rationality, which takes these factors into consideration. It also explores how resource limitations further affect the agents reasoning in this context. 2. Shortcomings of individual rationality in multi-agent systems The successful combination of several autonomous, intelligent agents working together is the aim of research into multi-agent systems [6], [7]. Increasingly, the multi-agent paradigm is being used to build real, complex systems [8], [9]. Reasons for this include the inherent natural distribution of problem components and the maturing of distributed computing technology. In such systems, the agents are interdependent, due to resource limitations and problem interdependencies, andl need to interact with one another in order to achieve their goals. For example, due to lack of knowledge or problem