Logics of knowledge and action: critical analysis and challenges

International audienceWe overview the most prominent logics of knowledge and action that were proposed and studied in the multiagent systems literature. We classify them according to these two dimensions, knowledge and action, and moreover introduce a distinction between individual knowledge and group knowledge, and between a nonstrategic an a strategic interpretation of action operators. For each of the logics in our classification we highlight problematic properties. They indicate weaknesses in the design of these logics and call into question their suitability to represent knowledge and reason about it. This leads to a list of research challenges

A branch-and-bound method for reversed geometric programming. Oper. Res. (USA).

A general or signomial geometric program is a nonlinear mathematical program involving general polynomials in several variables both in the objective function and the constraints. A branch-and-bound method is proposed for this extensive class of nonconvex optimization program guaranteeing convergence to the global optimum. The subproblems to be solved are convex but the method can easily be combined with a cutting plane technique to generate subproblems which are linear. A simple example is given to illustrate the technique.

A branch-and-bound method for reversed geometric programming

A general or signomial geometric program is a nonlinear mathematical program involving general polynomials in several variables both in the objective function and the constraints. A branch-and-bound method is proposed for this extensive class of nonconvex optimization program guaranteeing convergence to the global optimum. The subproblems to be solved are convex but the method can easily be combined with a cutting plane technique to generate subproblems which are linear. A simple example is given to illustrate the technique.Anglai

Multigroup discriminant analysis using linear programming

In this paper we introduce a non-parametric linear programming formulation for the general multigroup classification problem. Previous research using linear programming formulations has either been limited to the two-group case, or required complicated constraints and many zero-one variables. We develop general properties of our multigroup formulation and illustrate its use with several small example problems and previously published real data sets. A comparative analysis on the real data sets shows that our formulation may offer an interesting robust alternative to parametric statistical formulations for the multigroup discriminant problem