Jacqueline Jarvis

Holonic Execution: A BDI Approach

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Implementing a multi-agent systems approach to collaborative autonomous manufacturing operations

Remote space operations is a very demanding domain for software systems, requiring the characteristics of high availability, robustness, autonomy, real-time or near real-time response times, ease of reprogramming, and low computational footprint. A further desirable characteristic is the ability for remote systems or vehicles to collaborate to achieve tasks such as exploration or remote processing or sensing. Intelligent agent technology has advanced to the point where it is now finding application in robotic manufacturing, such as the application described in this paper. The authors believe that many of the lessons being learned in the collaborative manufacturing domain have relevance to space operations. This paper describes a novel agent-based execution architecture. Integral to this architecture is the separation of part processing concerns from manufacturing concerns. Thus we have part agents which are able to issue and track resource-independent part-processing requests. An interface agent then transforms a resource-independent request into one or more resource-dependent requests and dispatches them to the appropriate manufacturing agents. Execution of the dispatched requests then proceeds autonomously, taking into account safety constraints. We also describe an implementation of this architecture for a robotic assembly cell located at the University of Cambridge using JACK Intelligent Agents/sup TM/. The paper concludes with the lessons learned from this experiment, and highlights those of relevance to the domain of space operations.

Holonic Diagnosis for an Automotive Assembly Line

Diagnosis is an important function of a holonic manufacturing system if the desired levels of stability, adaptability and flexibility are to be achieved. Our research agenda is to study holonic behaviours (such as diagnosis and control) through the incorporation of these behaviours into operational industrial systems. Given the lack of fielded holonic solutions in industry, we are currently constrained to use conventional systems in our work. In this paper we describe the development of a holonic diagnostic capability for a PLC-controlled vehicle assembly line. A novel model-based strategy is used for diagnosis. Because of the constraints imposed on model formation in this environment, a two-phase approach consisting of off-line fault space generation and online fault space analysis is used. The fault space analysis utilises heuristics to achieve the desired performance levels (diagnosis in less than 60 seconds and success rates of greater than 90%). Areas for further research in holonic diagnosis are identified.

Multiagent Systems and Applications

Since its conception almost 30 years ago, the BDI (Belief Desire Intention) model of agency has become established, along with Soar, as the approach of choice for practitioners in the development of knowledge intensive agent applications. However, in developing BDI agent applications for over 15 years, the authors of this book have observed a disconnect between what the BDI model provides and what is actually required of an agent model in order to build practical systems. The GORITE BDI framework was developed to address this gap and this book is written for students, researchers and practitioners who wish to gain a practical understanding of how GORITE is used to develop BDI agent applications. In this regard, a feature of the book is the use of complete, annotated examples. As GORITE is a Java framework, a familiarity with Java (or a similar language) is assumed, but no prior knowledge of the BDI model is required.

Design Recovery for PLC-Controlled Manufacturing Systems

Abstract Design recovery has a potentially important role to play in the maintenance and redesign of complex PLC controlled manufacturing systems. We believe that if the finite state behaviour of such systems can be efficiently constructed, we get a representation which is closer to the designers original intent. Thus the understanding of an existing system which is an essential component of maintenance and redesign is facilitated. The opportunity also exists to use the finite state representation as the basis for formal analysis of system behaviour and also to replace the existing control system with one based directly on the finite state representation. In this paper we describe an approach which has enabled the finite state behaviour to be extracted from a behavioural model of a 700 i/o point, 3 station assembly line.

Multi-Agent Systems

Intelligent agent technology is at an intriguing stage in its development. Commercial strength agent applications are increasingly being developed in domains as diverse as manufacturing (Deen, 2003; Bussman et al., 2004; Jarvis et al., 2008a) war gaming (Jones et al., 1999; Heinze et al., 2002) and UAV mission management (Karim and Heinze, 2005). Furthermore industrial strength development environments are available, e.g. (AOS Group, 2012; University of Michigan, 2012; JADE, 2012) and design methodologies (Padgham and Winikoff, 2004) reference architectures (van Brussel et al., 1998) and standards (IEEE Computer Society, 2012) are beginning to appear.

Using agent teams to model enterprise behaviour

Service Oriented Architectures have enabled enterprise architectures to be composed as loosely coupled collections of applications that interact using platform independent web services and standards. Enterprise behaviour is then traditionally modelled as workflows, with business requests invoking specific enterprise services. In this paper, we propose an alternative approach, where enterprise behaviour is modelled as goals to be achieved by dynamically formed teams. These teams can be formed within an individual enterprise or can span enterprises, thus enabling virtual enterprises to be explicitly modelled. Team behaviours are specified independently of the actual services that are available, thus providing a clear separation between behaviour specification (business process definition) and behaviour execution (business process operation). The team modelling framework that is used is JACK™ Teams, which is a component of the JACK™ Intelligent Agents product suite [1].

A flexible plan step execution model for BDI agents

For the past 20 years, BDI (Belief, Desire, Intention) frameworks such as PRS [1], dMARS [2] and JACK [3] have provided, together with Soar [4], the two environments of choice for the development of knowledge rich, industrial strength intelligent agent applications [5]. However, we have observed that while the BDI model of plan execution works well for the tactical reasoning component of such applications, operational reasoning often requires a richer execution model. In this paper, we present an alternative, but complementary model for plan step execution by BDI agents. In the BDI model, plan steps either succeed or fail; if a plan step fails, then the plan fails and reconsideration of the current goal may occur. We have found that this approach is problematic when used for applications where resource contention is a regular occurrence, such as in manufacturing execution [6]. In these situations, it is necessary to review progress after each step, regardless of the step outcome. Our alternative model for plan step execution allows for the explicit modelling of the plan step lifecycle and the utilisation of infrastructure to manage the progression of that lifecycle. The model is realised using the JACK™ Intelligent Agents (JACK) product suite [3] and its feasibility is demonstrated through the development of an execution system for a robotic assembly cell.

Human-Agent Collaboration: A Goal-Based BDI Approach

The Belief-Desire-Intention (BDI) model of agency has been a popular choice for the modelling of goal-based behaviour for both individual agents and more recently, teams of agents. Numerous frameworks have been developed since the model was first proposed in the early 1980s. However, while the more recent frameworks support a delegative model of agent/agent and human/agent collaboration, no frameworks support a general model of collaboration. Given the importance of collaboration in the development of practical semi-autonomous agent applications, we consider this to constitute a major limitation of traditional BDI frameworks. In this paper, we present GORITE, a novel BDI framework that by employing explicit goal representations, overcomes many of the limitations of traditional frameworks. In terms of human/agent collaboration, key requirements are identified and through the use of a representative but simple example, the ability of GORITE to address those requirements is demonstrated.

Getting Started with GORITE

Goal Oriented Teams (GORITE) is a Java framework for the implementation of goal oriented process models in a team oriented paradigm (Ronnquist, 2012). Both the design and implementation of GORITE is representative of industry best practice. The framework is also the subject of ongoing maintenance and enhancement - the current release (June 2012) is v9RC04. Given its primary design objective, GORITE is provided as a Java class library and there is currently no graphical development environment. However, in contrast to conventional BDI frameworks, there is no separate plan language - all development is in Java. Consequently, developers can continue to use their favourite IDE.