Social behaviour arises as a result of individual agents cooperating with each other so as to exploit the resources available in a rich and dynamic multi-agent domain. If agents are to make use of others to help them in their tasks, such social behaviour is critical. Underlying this cooperation is the transfer or adoption of goals from one agent to another, a subtle and complex process that depends on the nature of the agents involved. In this paper we analyse this process by building upon a hierarchy previously constructed to define objects, agents and autonomous agents. We describe the motivated self-generation of goals that defines agent autonomy and the adoption of goals between agents that enables social behaviour. Then we consider three classes of goal adoption by objects, agents and autonomous agents. The first of these is merely a question of instantiation, the second requires an understanding of the relationship of the agent to others that are engaging it, and the third amounts to a question of negotiation or persuasion.

this paper, a network of the former type will be analyzed in the following. Figure 24 shows a characteristic trajectory of a successful robot controller of this type. As above, the robot starts off facing the upper left obstacle. It turns away from it to the left, enters the zone, and collects three objects on its first pass through the zone, turning slightly to the left towards each of them. As soon as it has left the zone it starts moving in a semi-circle to the left, which takes it back into the zone. In the zone it starts moving straight ahead again, takes a slight turn to the right to collect the upper object, and continues straight ahead out of the zone. The same pattern is repeated: as soon as it leaves the zone, it moves in a semi-circle to the left, which takes it back into the zone, where it starts moving straight forward again. Once more it performs a slight turn to the right to collect an object it would otherwise have missed. It continues to move straight ahead, leaves the zone, returns in another semi-circle, enters once more and moves straight ahead until the evaluation period ends.

This paper gives an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on life-like qualities in both agents and environments.

When an agent can operate at different levels of autonomy, factors in the environment, the domain, and the goals of the agent influence what level of autonomy the agent should exhibit. However, research into what metrics are appropriate and what environmental factors might justify changes in autonomy are scarce. This paper attempts to establish the relationships between uncertainty in the environment and the most effective level of autonomy for an agent to exhibit. The degree of autonomy of an agent can be defined as the degree to which that agent can produce and implement effective plans to meet goals. In a perfect world, all feasible plans succeed, and therefore agents can be completely autonomous. It is generally accepted that as the uncertainty and dynamism in the world increase the effectiveness of plans decreases. If an agent must meet mission critical goals in such a domain, the agent may need to rely on external control. The degree to which external control is required can be...

In this paper, we describe a new application domain for intelligent autonomous systems – Intelligent Buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. The fuzzy rules related to the room resident comfort are learnt and adapted online using our patented Fuzzy-Genetic techniques (British Patent 99-10539.7). The learnt rule base is updated and adapted via an iterative machine-user dialogue. This learning starts from the best stored rule set in the agent memory (Experience Bank) thereby decreasing the learning time and creating an intelligent agent with memory. We discuss the role of learning in building control systems, and we explain the importance of acquiring information from sensors, rather than relying on pre-programmed models, to determine user needs. We describe how our architecture, consisting of distributed embedded agents, utilises sensory information to learn to perform tasks related to user comfort, energy conservation, and safety. We show how these agents, employing a behaviour-based approach derived from robotics research, are able to continuously learn and adapt to individuals within a building, whilst always providing a fast, safe response to any situation. In addion we show that our system learns similar rules to other offline supervised methods but that our system has the addional capability to rapidly learn and optimise the learnt rule base. Applications of this system include personal support (e.g. increasingr independence and quality of life for older people), energy efficincy in commercial buildings or living-area control systems for space vehicles and planetary habitation modules

Introduction Autonomy is perplexing. It is recognisably and undeniably a critical issue in the field of intelligent agents and multi-agent systems, yet it is often ignored or simply assumed. For many, agents are autonomous by definition, and they see no need to add the tautologous prefix in explicitly considering autonomous agents, while for others autonomy in agents is an important yet problematic issue that demands attention. The difficulty when considering autonomy, however, is that there are different conceptual levels at which to reason and argue, including the philosophical and the practical. The notion of autonomy has associated with it many variations of meaning. According to Steels, autonomous systems must be automatic systems and, in addition, they must have the capacity to form and adapt their behaviour while operating in the environment. Thus traditional AI systems and most robots are automatic but not autonomous --- they are not independent

Agents are abstractions for real-world autonomous and cooperating entities. Agent-oriented software development is the most promising approach for the development of complex systems. On the other hand, Petri nets are a good formalism for describing concurrency, synchronisation and causality. After presenting the notion of agents, we present some thoughts on using nets in agent-oriented development of dynamic and flexible software systems. The cover picture shows the model of an imaginary system according to our ideas about combining agents and nets. 1 Introduction Several research disciplines have been searching for an answer to the same fundamental question: "How should we understand the real-world ?" This is a wide-ranging and very intriguing question. For psychologists, an adequate answer to this question would help in describing behaviour of humans in several contexts, such as children in a family situation, adult relationships, religious behaviour, and so on. In the domain of art...

... or she is assigned to a new job. The Navy employs some 280 people, called detailers, full time to effect these new assignments. The IDA (Intelligent Distribution Agent) prototype was designed and built to automate, in a cognitively plausible manner, the job of the human detailers. That model is being redesigned to function as a multi-agent system. This is not a trivial matter due to the fact that there would need to be approximately 350,000 individual agents. There are also many issues relating to how the agents interact and how all entities involved, including humans, exercise their autonomy. This paper describes both the IDA prototype and the Multi-Agent IDA system being created from it. We will also discuss several of the major issues regarding the design, interaction, and autonomy of the various agents involved.

One of the key aspects of most living organisms is their ability to detect and exploit natural sources of energy within their environment. We are currently developing a robotic predator system that will attempt to sustain itself by hunting and catching slugs on agricultural land. A microbial fuel cell will be used to convert the slug bio-mass to electricity thus providing the robot's energy supply. This paper outlines the requirements for such a predator and describes recent progress on the robot. We also present data from trials of the robot hunting and catching slugs in a situation similar to that found in an agricultural field.

This paper addresses the problem of implementing agent-based software systems with respect to agent framework fundamental concepts such as autonomy and interaction without specifying any particular agent internal architecture. The autonomy