Agent-based distributed software verification

Despite decades of research, the takeup of formal methods for developing provably correct software in industry remains slow. One reason for this is the high cost of proof construction, an activity that, due to the complexity of the required proofs, is typically carried out using interactive theorem provers. In this paper we propose an agent-oriented architecture for interactive theorem proving with the aim of reducing the user interactions (and thus the cost) of constructing software verification proofs. We describe a prototype implementation of our architecture and discuss its application to a small, but non-trivial case study.

A Reactive Agent-based Neural Network Car Following Model

This paper presented a novel approach to develop car following models using reactive agent techniques for mapping perceptions to actions. The results showed that the model outperformed the Gipps and Psychophysical family of car following models. The standing of this work is highlighted by its acceptance and publication in the proceedings of the International IEEE Conference on Intelligent Transportation Systems (ITS), which is now recognised as the premier international conference on ITS. The paper acceptance rate to this conference was 67 percent. The standing of this paper is also evidenced by its listing in international databases like Ei Inspec and IEEE Xplore. The paper is also listed in Google Scholar. Dr Dia co-authored this paper with his PhD student Sakda Panwai.

Design and development of agent-based procurement system to enhance business intelligence

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.

Design and development of an agent-based information system for process improvement

To meet changing needs of customers and to survive in the increasingly globalised and competitive environment, it is necessary for companies to equip themselves with intelligent tools, thereby enabling managerial levels to use the tactical decision in a better way. However, the implementation of an intelligent system is always a challenge in Small- and Medium-sized Enterprises (SMEs). Therefore, a new and simple approach with 'process rethinking' ability is proposed to generate ongoing process improvements over time. In this paper, a roadmap of the development of an agent-based information system is described. A case example has also been provided to show how the system can assist non-specialists, for example, managers and engineers to make right decisions for a continual process improvement. Copyright © 2006 Inderscience Enterprises Ltd.

Achieving cost competitiveness with an agent based integrated process planning and production scheduling system

From a manufacturing perspective, the efficiency of manufacturing operations (such as process planning and production scheduling) are the key element for enhancing manufacturing competence. Process planning and production scheduling functions have been traditionally treated as two separate activities, and have resulted in a range of inefficiencies. These include infeasible process plans, non-available/overloaded resources, high production costs, long production lead times, and so on. Above all, it is unlikely that the dynamic changes can be efficiently dealt with. Despite much research has been conducted to integrate process planning and production scheduling to generate optimised solutions to improve manufacturing efficiency, there is still a gap to achieve the competence required for the current global competitive market. In this research, the concept of multi-agent system (MAS) is adopted as a means to address the aforementioned gap. A MAS consists of a collection of intelligent autonomous agents able to solve complex problems. These agents possess their individual objectives and interact with each other to fulfil the global goal. This paper describes a novel use of an autonomous agent system to facilitate the integration of process planning and production scheduling functions to cope with unpredictable demands, in terms of uncertainties in product mix and demand pattern. The novelty lies with the currency-based iterative agent bidding mechanism to allow process planning and production scheduling options to be evaluated simultaneously, so as to search for an optimised, cost-effective solution. This agent based system aims to achieve manufacturing competence by means of enhancing the flexibility and agility of manufacturing enterprises.

Agent Based Models of Competition and Collaboration

Swarm intelligence is a popular paradigm for algorithm design. Frequently drawing inspiration from natural systems, it assigns simple rules to a set of agents with the aim that, through local interactions, they collectively solve some global problem. Current variants of a popular swarm based optimization algorithm, particle swarm optimization (PSO), are investigated with a focus on premature convergence. A novel variant, dispersive PSO, is proposed to address this problem and is shown to lead to increased robustness and performance compared to current PSO algorithms. A nature inspired decentralised multi-agent algorithm is proposed to solve a constrained problem of distributed task allocation. Agents must collect and process the mail batches, without global knowledge of their environment or communication between agents. New rules for specialisation are proposed and are shown to exhibit improved eciency and exibility compared to existing ones. These new rules are compared with a market based approach to agent control. The eciency (average number of tasks performed), the exibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved eciency and robustness. Evolutionary algorithms are employed, both to optimize parameters and to allow the various rules to evolve and compete. We also observe extinction and speciation. In order to interpret algorithm performance we analyse the causes of eciency loss, derive theoretical upper bounds for the eciency, as well as a complete theoretical description of a non-trivial case, and compare these with the experimental results. Motivated by this work we introduce agent "memory" (the possibility for agents to develop preferences for certain cities) and show that not only does it lead to emergent cooperation between agents, but also to a signicant increase in efficiency.