Integrating Abstract State Machines and Interpreted Systems for Situation Analysis decision support design

A formal approach to the design of situation analysis and decision support systems is justified and unavoidable if one is interested in reproducibility/traceability of results, satisfaction of constraints, and a language to represent and reason about dynamic situations. In this paper, we propose the integration of two multiagent modeling paradigms, abstract state machines and interpreted systems, to develop a comprehensive framework for computational situation analysis (SA) as a basis for design and development of decision support systems. Due to the similarities of the underlying modeling concepts, a systematic integration of the two paradigms seems sensible, as each one has its particular focus and strength, complementing each other in several respects. Our approach builds on multiagent systems theories to formalize the distributed aspect, allows for reasoning about knowledge, uncertainty and belief change, and enables rapid prototyping of abstract executable decision support system models.