An Agent-based approach to modelling integrated product teams undertaking a design activity.

The interactions between individual designers, within integrated product teams, and the nature of design tasks, all have a significant impact upon how well a design task can be performed, and hence the quality of the resultant product and the time in which it can be delivered. In this paper we describe an ongoing research project which aims to model integrated product teams through the use of multi-agent systems. We first describe the background and rationale for our work, and then present our initial computational model and results from the simulation of an integrated product team. The paper concludes with a discussion of how the model will evolve to improve the accuracy of the simulation

Information requirements for enterprise systems

In this paper, we discuss an approach to system requirements engineering, which is based on using models of the responsibilities assigned to agents in a multi-agency system of systems. The responsibility models serve as a basis for identifying the stakeholders that should be considered in establishing the requirements and provide a basis for a structured approach, described here, for information requirements elicitation. We illustrate this approach using a case study drawn from civil emergency management

MULTI AGENT-BASED ENVIRONMENTAL LANDSCAPE (MABEL) - AN ARTIFICIAL INTELLIGENCE SIMULATION MODEL: SOME EARLY ASSESSMENTS

The Multi Agent-Based Environmental Landscape model (MABEL) introduces a Distributed Artificial Intelligence (DAI) systemic methodology, to simulate land use and transformation changes over time and space. Computational agents represent abstract relations among geographic, environmental, human and socio-economic variables, with respect to land transformation pattern changes. A multi-agent environment is developed providing task-nonspecific problem-solving abilities, flexibility on achieving goals and representing existing relations observed in real-world scenarios, and goal-based efficiency. Intelligent MABEL agents acquire spatial expressions and perform specific tasks demonstrating autonomy, environmental interactions, communication and cooperation, reactivity and proactivity, reasoning and learning capabilities. Their decisions maximize both task-specific marginal utility for their actions and joint, weighted marginal utility for their time-stepping. Agent behavior is achieved by personalizing a dynamic utility-based knowledge base through sequential GIS filtering, probability-distributed weighting, joint probability Bayesian correlational weighting, and goal-based distributional properties, applied to socio-economic and behavioral criteria. First-order logics, heuristics and appropriation of time-step sequences employed, provide a simulation-able environment, capable of re-generating space-time evolution of the agents.Environmental Economics and Policy,