We discuss a procedure which extracts statistical and entropic information from data in order to discover Boolean rules underlying them. We work within a granular computing framework where logical implications between statistics on the observed sample and properties on the whole data population are stressed in terms of both probabilistic and possibilistic measures of the inferred rules. With the main constraint that the class of rules is not known in advance, we split the building of the hypotheses on them in various levels of increasing description complexity, balancing the feasibility of the learning procedure with the understandability and reliability of the formulas that are discovered. We appreciate the entire learning system in terms of truth tables, formula lengths, and computational resources through a set of case studies

We present an approach for the automatic definition of the fuzzy rules for a fuzzy controller based on the use of the tabu search (TS) scheme. We show also how the application of the TS process to the learning of a fuzzy rule base can be improved using heuristic symbolic meta rules. The paper is divided in two parts. The first part presents an introduction to TS and different learning schemes which can be used to apply it for the determination of the fuzzy control rules. The second part illustrates the application of the proposed techniques to a specific control problem-the parking of a truck and trailer. In particular, Section V illustrates the definition of a rule base for a static fuzzy controller, while Section VI presents the construction of an adaptive parking controller.

We exploit the two distinguishing features of Boltzmann machines-plasticity of connections and concentration of equilibrium states around the minima of a loss function-for designing an optimization algorithm where the neural network learns to suitably locate its equilibrium state on the target of the optimization problem. The algorithm is structured in two loops: an inner loop catches the equilibrium state providing reliable optima of a relaxed issue of the optimization problem; the outer loop modifies the weights of the neural network in order to make these solutions meanly translate into very fine approximated solutions of the exact optimization problem. The two loops have asymptotical trajectories drawn by the optima of the exact problem. We tested the algorithm on a simple non-preemptive job shop problem using the Lawrence benchmark. A trade-off analysis between running time and approximation accuracy shows that in many cases the algorithm achieves an accuracy of the same order of the best simulated annealing, taking at most one order higher running time, and does not require any user intervention to assess the parameters of the optimization process.

The need of supporting office work with suitable computer based tools implies the investigation of the deep aspects of cooperation within the office. Cooperation, to the extent that is made up of communication and coordination, can be fully characterized under the assumption that an office is a special linguistic game, constituted by a set of rules defining the conversations possible within it, continuously changing under the perturbations created by the speech acts its member do performing the conversations. Within this conceptual context, a prototypal software package, CHAOS-1, is presented. CHAOS-1 aims both at supporting the conversations and at improving coordination of the office activities.This paper discusses CHAOS-1 with respect to similar proposals, presents its overall architecture, shows some examples of its use and sketches its new releases under development.