Interpreted systems for situation analysis

Abstract

This paper details and deepens a previous work where the Interpreted Systems semantics was proposed as a general framework for situation analysis (SA). This framework is particularly efficient for representing and reasoning about knowledge and uncertainty when performing situation analysis tasks. Our approach of SA is to base our analysis on the production of state transition systems consisting in the set of all temporal trajectories possibly obtained upon the execution of a given set of agents' protocols. Thus seen, the SA task involves the definition of more or less subtle reasoning about graph structures. A formal situation analysis model is defined as an interpreted algorithmic belief change system. In such a model, the notions of situation, situation awareness and situation analysis are provided. The analysis of the situation is done through the verification of implicit notions of knowledge with temporal properties. Implicit knowledge is distinguished from explicit knowledge and situation awareness is defined in terms of the computing power of resource-bounded agents. A general plausibility measure allows us to model belief while making the link with quantitative representations of uncertainty such as probabilities, belief functions and possibilities. The propsed modelisation of the Situation Analysis process, while compatible with the traditionnal implicit representation of knowledge found in modal logic, allows us to link the decision processes of the agents, their awareness of the situation with the observations they make about the environment.