Mark d'Inverno

A Formal Specification of dMARS

The Procedural Reasoning System (PRS) is the best established agent architecture currently available. It has been deployed in many major industrial applications, ranging from fault diagnosis on the space shuttle to air traffic management and business process control. The theory of PRS-like systems has also been widely studied: within the intelligent agents research community, the belief-desire-intention (BDI) model of practical reasoning that underpins PRS is arguably the dominant force in the theoretical foundations of rational agency. Despite the interest in PRS and BDI agents, no complete attempt has yet been made to precisely specify the behaviour of real PRS systems. This has led to the development of a range of systems that claim to conform to the PRS model, but which differ from it in many important respects. Our aim in this paper is to rectify this omission. We provide an abstract formal model of an idealised dMARS system (the most recent implementation of the PRS architecture), which precisely defines the key data structures present within the architecture and the operations that manipulate these structures. We focus in particular on dMARS plans, since these are the key tool for programming dMARS agents. The specification we present will enable other implementations of PRS to be easily developed, and will serve as a benchmark against which future architectural enhancements can be evaluated.

The dMARS Architecture: A Specification of the Distributed Multi-Agent Reasoning System

The Procedural Reasoning System (PRS) is the best established agent architecture currently available. It has been deployed in many major industrial applications, ranging from fault diagnosis on the space shuttle to air traffic management and business process control. The theory of PRS-like systems has also been widely studied: within the intelligent agents research community, the belief-desire-intention (BDI) model of practical reasoning that underpins PRS is arguably the dominant force in the theoretical foundations of rational agency. Despite the interest in PRS and BDI agents, no complete attempt has yet been made to precisely specify the behaviour of real PRS systems. This has led to the development of a range of systems that claim to conform to the PRS model, but which differ from it in many important respects. Our aim in this paper is to rectify this omission. We provide an abstract formal model of an idealised dMARS system (the most recent implementation of the PRS architecture), which precisely defines the key data structures present within the architecture and the operations that manipulate these structures. We focus in particular on dMARS plans, since these are the key tool for programming dMARS agents. The specification we present will enable other implementations of PRS to be easily developed, and will serve as a benchmark against which future architectural enhancements can be evaluated.

Constraining autonomy through norms

Despite many efforts to understand why and how norms can be incorporated into agents and multi-agent systems, there are still several gaps that must be filled. This paper focuses on one of the most important processes concerned with norms, namely that of norm compliance. However, instead of taking a static view of norms in which norms are straighforwardly complied with, we adopt a more dynamic view in which an agents motivations, and therefore its autonomy, play an important role. We analyse the motivations that an agent might have to comply with norms, and then formally propose a set of strategies for use by agents in norm-based systems. Finally, through some simulation experiments, the effects of autonomous norm compliance in both individual agents and societies are analysed.

Learning in multi-agent systems

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

Negotiation in multi-agent systems

In systems composed of multiple autonomous agents, negotiation is a key form of interaction that enables groups of agents to arrive at a mutual agreement regarding some belief, goal or plan, for example. Particularly because the agents are autonomous and cannot be assumed to be benevolent, agents must influence others to convince them to act in certain ways, and negotiation is thus critical for managing such inter-agent dependencies. The process of negotiation may be of many different forms, such as auctions, protocols in the style of the contract net, and argumentation, but it is unclear just how sophisticated the agents or the protocols for interaction must be for successful negotiation in different contexts. All these issues were raised in the panel session on negotiation.

Engineering AgentSpeak(L): A formal computational model

Perhaps the most successful agent architectures, and certainly the best known, are those based on the Belief-Desire-Intention (BDI) framework. Despite the wealth of research that has accumulated on both formal and practical aspects of this framework, however, there remains a gap between the formal models and the implemented systems. In this paper, we build on earlier work by Rao aimed at narrowing this gap, by developing a strongly-typed, formal, yet computational model of the BDI-based AgentSpeak(L) language. AgentSpeak(L) is a programming language, based on the Procedural Reasoning System (PRS) and the Distributed Multi-Agent Reasoning System (dMARS), which determines the behaviour of the agents it implements. In developing the model, we add to Raos work, identify some omissions, and progress beyond the description of a particular language by giving a formal specification of a general BDI architecture that can be used as the basis for providing further formal specifications of more sophisticated systems.

From Agent Theory to Agent Construction: A Case Study

There is a growing body of work that concentrates on theoretical aspects of agents and multi-agent systems, and a complementary body of work concerned with building practical systems. However, the two have typically been unrelated. This gap between the theory and practice of intelligent agents has only relatively recently begun to be addressed. In this paper we describe the construction of an agent simulation environment that is based strongly on a formal theory of agent systems, but which is intended to serve in exactly this way as a basis for practical development. The paper briefly introduces the theory, then describes the system and the simple reactive agents built with it, but most importantly shows how it reflects the theoretical framework and how it facilitates incremental agent design and implementation. Using this example as a case-study, some possibilities for a methodology for the development of agent systems are discussed.

A Conceptual Framework for Agent Definition and Development

The use of agents of many different kinds in a variety of fields of computer science and artificial intelligence is increasing rapidly and is due, in part, to their wide applicability. The richness of the agent metaphor that leads to many different uses of the term is, however, both a strength and a weakness: its strength lies in the fact that it can be applied in very many different ways in many situations for different purposes; the weakness is that the term agent is now used so frequently that there is no commonly accepted notion of what it is that constitutes an agent. This paper addresses this issue by applying formal methods to provide a defining framework for agent systems. The Z specification language is used to provide an accessible and unified formal account of agent systems, allowing us to escape from the terminological chaos that surrounds agents. In particular, the framework precisely and unambiguously provides meanings for common concepts and terms, enables alternative models of particular classes of system to be described within it, and provides a foundation for subsequent development of increasingly more refined concepts.

Interaction protocols in Agentis

Agentis is a framework for building interactive multiagent applications which is based upon a model of agent interaction whose key elements are services and tasks. Central to the operation of the system is the set of protocols that permit reliable, concurrent request and provision of services and tasks from and to agents, using an underlying asynchronous point-to-point messaging infrastructure. In this paper we focus on this aspect of the Agentis system and provide a detailed description of these protocols, together with a formal specification in Z. The specification can be seen as part of a more complete formal specification of the entire system, which provides an integrated and coherent way of describing the system at different levels. In so specifying the Agentis protocols, however we also provide some general guidelines which may be applied to the specification of other protocols for agent interaction.

Communicating open systems

Just as conventional institutions are organisational structures for coordinating the activities of multiple interacting individuals, electronic institutions provide a computational analogue for coordinating the activities of multiple interacting software agents. In this paper, we argue that open multi-agent systems can be effectively designed and implemented as electronic institutions, for which we provide a comprehensive computational model. More specifically, the paper provides an operational semantics for electronic institutions, specifying the essential data structures, the state representation and the key operations necessary to implement them. We specify the agent workflow structure that is the core component of such electronic institutions and particular instantiations of knowledge representation languages that support the institutional model. In so doing, we provide the first formal account of the electronic institution concept in a rigorous and unambiguous way.