Michael Winikoff

Prometheus: a methodology for developing intelligent agents

As agents gain acceptance as a technology there is a growing need for practical methods for developing agent applications. This paper presents the Prometheus methodology, which has been developed over several years in collaboration with Agent Oriented Software. The methodology has been taught at industry workshops and university courses. It has proven effective in assisting developers to design, document, and build agent systems. Prometheus differs from existing methodologies in that it is a detailed and complete (start to end) methodology for developing intelligent agents which has evolved out of industrial and pedagogical experience. This paper describes the process and the products of the methodology illustrated by a running example.

JACK intelligent agents: an industrial strength platform

Software agents offer a range of benefits to the development of complex software systems. However, before these benefits can be realised by the computing industry there is a need for an agent platform that can be accepted by industry. In this paper we describe the JACK agent platform: a mature and robust commercial product. We argue that JACK meets requirements such as familiarity, scalability and integratibility which make it suitable for adoption by industry. We also describe interesting features of JACK such as the use of capabilities for structuring agents, and JACK’s approach to teamwork which allows hierarchical team structures.

Adding debugging support to the Prometheus methodology

This paper describes a debugger which uses the design artifacts of the Prometheus agent-oriented software engineering methodology to alert the developer testing the system, that a specification has been violated. Detailed information is provided regarding the error which can help the developer in locating its source. Interaction protocols specified during design, are converted to executable Petri net representations. The system can then be monitored at run time to identify situations which do not conform to specified protocols. A process for monitoring aspects of plan selection is also described. The paper then describes the Prometheus Design Tool, developed to support the Prometheus methodology, and presents a vision of an integrated development environment providing full life cycle support for the development of agent systems. The initial part of the paper provides a detailed summary of the Prometheus methodology and the artifacts on which the debugger is based.

Simplifying the Development of Intelligent Agents

Intelligent agents is a powerful Artificial Intelligence technology which shows considerable promise as a new paradigm for mainstream software development. However, despite their promise, intelligent agents are still scarce in the market place. A key reason for this is that developing intelligent agent software requires significant training and skill: a typical developer or undergraduate struggles to develop good agent systems using the Belief Desire Intention (BDI) model (or similar models). This paper identifies the concept set which we have found to be important in developing intelligent agent systems and the relationships between these concepts. This concept set was developed with the intention of being clearer, simpler, and easier to use than current approaches. We also describe briefly a (very simplified) example from one of the projects we have worked on (RoboRescue), illustrating the way in which these concepts are important in designing and developing intelligent software agents.

Implementing commitment-based interactions

Although agent interaction plays a vital role in MAS, and message-centric approaches to agent interaction have their drawbacks, present agent-oriented programming languages do not provide support for implementing agent interaction that is flexible and robust. Instead, messages are provided as a primitive building block. In this paper we consider one approach for modelling agent interactions: the commitment machines framework. This framework supports modelling interactions at a higher level (using social commitments), resulting in more flexible interactions. We investigate how commitment-based interactions can be implemented in conventional agent-oriented programming languages. The contributions of this paper are: a mapping from a commitment machine to a collection of BDI-style plans; extensions to the semantics of BDI programming languages; and an examination of two issues that arise when distributing commitment machines (turn management and race conditions) and solutions to these problems.

Tool support for agent development using the Prometheus methodology

We believe that tool support is very important for any methodology. In this paper we describe PDT (Prometheus design tool) which supports the design of an intelligent agent system using the Prometheus methodology. We describe how PDT supports the various stages of Prometheus through various means such as consistency checking, support for entity propagation, and hierarchical views. We also describe works that are currently in progress which involves the development of a plug-in for Eclipse with the aim of creating a single integrated development environment which would support the complete development cycle of an agent system from design to deployment.

Enhancing commitment machines

Agent interaction protocols are usually specified in terms of permissible sequences of messages. This representation is, unfortunately, brittle and does not allow for flexibility and robustness. The commitment machines framework of Yolum and Singh aims to provide more flexibility and robustness by defining interactions in terms of the commitments of agents. In this paper we identify a number of areas where the commitment machines framework needs improvement and propose an improved version. In particular we improve the way in which commitments are discharged and the way in which pre-conditions are specified.

Detecting & exploiting positive goal interaction in intelligent agents

Rational agents typically pursue multiple goals in parallel. However most existing agent systems do not have any infrastructure support for reasoning about either positive or negative interactions between goals. Negative interactions include such things as competition for resources, which if unrecognised can lead to unnecessary failure of both goals requiring the resource. Positive interactions include situations where there is potentially a common subgoal of two goals. This paper looks at mechanisms for identifying potential common subgoals, and attempting to schedule the actions of the agent to take advantage of this. Potential common subgoals are identified by maintaining summaries of definite and potential effects of goals and plans to achieve those goals. Template summaries for goal types are produced at compile time, while instance summaries are maintained and updated at execution time to allow the agent to choose and schedule its plans to take advantage of potential commonality where possible. This increases the ability of the agent to act in a rational manner, where rational is loosely defined as the sensible behaviour exhibited by humans.

Programming in Lygon: An Overview

For many given systems of logic, it is possible to identify, via systematic proof-theoretic analyses, a fragment which can be used as a basis for a logic programming language. Such analyses have been applied to linear logic, a logic of resource-consumption, leading to the definition of the linear logic programming language Lygon. It appears that (the basis of) Lygon can be considered to be the largest possible first-order linear logic programming language derivable in this way. In this paper, we describe the design and application of Lygon. We give examples which illustrate the advantages of resource-oriented logic programming languages.

Principles and Practice of Multi-Agent Systems

This special issue presents four revised and extended papers from PRIMA 2010: the 13th International Conference on Principles and Practice of Multi-Agent Systems, which was held in November 12–15, 2010, in Kolkata, India. Agent computing and technology is an exciting emerging paradigm that is expected to play a key role in many society-changing practices, from disaster response to manufacturing, and from energy management to healthcare. Agent systems are expected to operate in real-world environments, with all the challenges that such environments present. This special issue contains revised and extendedversions of four papers thatwere originally presented at the conference on Principles and Practice of Multi-Agent Systems (PRIMA). PRIMA has been held since 2007, and is successor to a workshop series (also called PRIMA, Pacific Rim International Workshop on Multi-Agents) which has been running since 1998. Each of the four papers in this special issue received high scores from reviewers, and they were given awards at the conference (best paper, two best paper runner-ups, and an IBM Research best paper award for a paper in the area of agents and services). Each of the four papers was invited to be extended, and was then reviewed by the original reviewers, and then checked by the chairs. The four papers cover a wide range of topics, and we hope that they capture the diversity and richness of work in the area, and specifically, of the work that was presented at PRIMA 2010. Effect of DisCSP Variable-Ordering Heuristics in Scale-free Networks, by Tenda Okimoto, Atsushi Iwasaki and Makoto Yokoo. This paper considers the long-standing problem of distributed constraint satisfaction in the case where the network structure of the problem has a particular structure, in this case being scale-free. The authors find that for scale-free networks the choice of variable-ordering heuristic is more significant than in random networks. The authors then develop a heuristic for ordering variables that is especially designed for scale-free networks, and that exhibits improved performance in such networks.