Intelligent agent for formal modelling of temporal multi-agent systems

Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system

Semantic web and formal design methods

Ph.DDOCTOR OF PHILOSOPH

Tools and verification techniques for integrated formal methods

Ph.DDOCTOR OF PHILOSOPH

Complementary formalisms - synthesis, verification and visualization

Ph.DDOCTOR OF PHILOSOPH

Applying Model Checking to Pervasive Computing Systems

Ph.DDOCTOR OF PHILOSOPH

Reasoning about complex agent knowledge - Ontologies, Uncertainty, rules and beyond

Ph.DDOCTOR OF PHILOSOPH

Verification of timed process algebra and beyond

Ph.DDOCTOR OF PHILOSOPH

A formalisation and prototype implementation of argumentation for statistical model selection

© 2019 – IOS Press and the authors. The task of data collection is becoming routine in many disciplines and this results in increased availability of data. This routinely collected data provides a valuable opportunity for analysis with a view to support evidence based decision making. In order to confidently leverage the data in support of decision making the most appropriate statistical method needs to be selected, and this can be difficult for an end user not trained in statistics. This paper outlines an application of argumentation to support the analysis of clinical data, that uses Extended Argumentation Frameworks in order to reason with the meta-level arguments derived from preference contexts relevant to the data and the analysis objective of the end user. We outline a formalisation of the argument scheme for statistical model selection, its critical questions and the structure of the knowledge base required to support the instantiation of the arguments and meta-level arguments through the use of Z notation. This paper also describes the prototype implementation of argumentation for statistical model selection based on the Z specification outlined herein.CONSULT EPSRC grant no. EP-P010105-1

Model checking concurrent and real-time systems : the PAT approach

Ph.DDOCTOR OF PHILOSOPH