Interaction Analysis in Smart Work Environments through Fuzzy Temporal Logic

Interaction analysis is defined as the generation of situation descriptions from machine perception. World models created through machine perception are used by a reasoning engine based on fuzzy metric temporal logic and situation graph trees, with optional parameter learning and clustering as preprocessing, to deduce knowledge about the observed scene. The system is evaluated in a case study on automatic behavior report generation for staff training purposes in crisis response control rooms

A logical and algebraic treatment of conditional probability

This paper is devoted to a logical and algebraic treatment of conditional probability. The main ideas are the use of non-standard probabilities and of some kind of standard part function in order to deal with the case where the conditioning event has probability zero, and the use of a many-valued modal logic in order to deal probability of an event phi as the truth value of the sentence phi is probable, along the lines of Hajek's book [H98] and of [EGH96]. To this purpose, we introduce a probabilistic many-valued logic, called FP(SLPi), which is sound and complete with respect a class of structures having a non-standard extension [0,1](star) of [0,1] as set of truth values. We also prove that the coherence of an assessment of conditional probabilities is equivalent to the coherence of a suitably defined theory over FP(SLPi) whose proper axioms reflect the assessment itself