Michael P. Georgeff

The Belief-Desire-Intention Model of Agency

Within the ATAL community, the belief-desire-intention (BDI) model has come to be possibly the best known and best studied model of practical reasoning agents. There are several reasons for its success, but perhaps the most compelling are that the BDI model combines a respectable philosophical model of human practical reasoning, (originally developed by Michael Bratman [1]), a number of implementations (in the IRMA architecture [2] and the various PRS-like systems currently available [7]), several successful applications (including the now-famous fault diagnosis system for the space shuttle, as well as factory process control systems and business process management [8]), and finally, an elegant abstract logical semantics, which have been taken up and elaborated upon widely within the agent research community [14, 16].

An architecture for real-time reasoning and system control

The Procedural Reasoning System, a generic reasoning system based on the rational-agent architecture, is described. The system has been applied to several real-time applications, including mobile robot control, system control for a surveillance aircraft, and air traffic management. Two applications that are relevant to process industries handling malfunctions for the reaction control system of NASAs Space Shuttle and diagnosing and controlling failures and overloads in a telecommunications network are discussed. Specifically, the representation and management of malfunction-handling procedures and tasks.<<ETX>>

A methodology and modelling technique for systems of BDI agents

The construction of large-scale embedded software systems demands the use of design methodologies and modelling techniques that support abstraction, inheritance, modularity, and other mechanisms for reducing complexity and preventing error. If multi-agent systems are to become widely accepted as a basis for large-scale applications, adequate agent-oriented methodologies and modelling techniques will be essential. This is not just to ensure that systems are reliable, maintainable, and conformant, but to allow their design, implementation, and maintenance to be carried out by software analysts and engineers rather than researchers. In this paper we describe an agent-oriented methodology and modelling technique for systems of agents based upon the Belief-Desire-Intention (BDI) paradigm. Our models extend existing Object-Oriented (OO) models. By building upon and adapting existing, well-understood techniques, we take advantage of their maturity to produce an approach that can be easily learnt and understood by those familiar with the OO paradigm.

Communication and interaction in multi-agent planning

A method for synthesizing multi-agent plans from simpler single-agent plans is described. The idea is to insert communication acts into the single-agent plans so that agents can synchronize activities and avoid harmful interactions. Unlike most previous planning systems, actions are represented by sequences of states, rather than as simple state change operators. This allows the expression of more complex kinds of interaction than would otherwise be possible. An efficient method of interaction and safety analysis is then developed and used to identify critical regions in the plans. An essential feature of the method is that the analysis is performed without generating all possible interleavings of the plans, thus avoiding a combinatorial explosion. Finally, communication primitives are inserted into the plans and a supervisor process created to handle synchronization.

Modelling and Design of Multi-Agent Systems

Agent technologies are now being applied to the development of large-scale commercial and industrial software systems. Such systems are complex, involving hundreds, perhaps thousands of agents, and there is a pressing need for system modelling techniques that permit their complexity to be effectively managed, and principled methodologies to guide the process of system design. Without adequate techniques to support the design process, such systems will not be sufficiently reliable, maintainable or extensible, will be difficult to comprehend, and their elements will not be re-usable.

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.

A theory of action for multi-agent planning

Abstract A theory of action suitable for reasoning about events in multiagent or dynamically changing environments is presented. A device called a process model is used to represent the observable behavior of an agent in performing an action. This model is more general than previous models of action, allowing sequencing, selection, nondeterminism, iteration, and parallelism to be represented. It is shown how this model can be utilized in synthesizing plans and reasoning about concurrency. In particular, conditions are derived for determining whether or not concurrent actions are free from mutual interference. It is also indicated how this theory provides a basis for understanding and reasoning about action sentences in both natural and programming languages.

Procedural control in production systems

Abstract This paper proposes a general production system architecture that allows procedural control knowledge to be directly represented and used. This architecture, called a controlled production system, is based on a separately specified control structure that effects control over production invocation and interaction independently of the search strategy. It is shown that a controlled production system provides a basis for describing and implementing control constructs which, unlike most existing schemes, is formally adequate and retains all the properties desired of a knowledge based system—modularity, flexibility, extensibility and explanatory capacity. We also show that this architecture provides for a uniform programming methodology—the procedural languages and the declarative languages turn out to be special cases of a controlled production system. Schemes for improving system efficiency and resolving nondeterminism are also examined. It is shown that the separate representation of control provides a basis for a theory of efficiency transformations on production systems, and allows for more effective means of directing search.

Deliberation and its role in the formation of intentions

Deliberation plays an important role in the design of rational agents embedded in the real-world. In particular, deliberation leads to the formation of intentions, i.e., plans of action that the agent is committed to achieving. In this paper, we present a branching time possible-worlds model for representing and reasoning about, beliefs, goals, intentions, time, actions, probabilities, and payoffs. We compare this possible-worlds approach with the more traditional decision tree representation and provide a transformation from decision trees to possible worlds. Finally, we illustrate how an agent can perform deliberation using a decision-tree representation and then use a possible-worlds model to form and reason about his intentions.

Active Databases and Agent Systems - A Comparison

This paper examines Active Databases and Agent Systems, comparing their purpose, structure, functionality, and implementation. Our presentation is aimed primarily at an audience familiar with active database technology. We show that they draw upon very similar paradigms in their quest to supply reactivity. This presents opportunities for migration of techniques and formalisms between the two fields.