In this paper, a Neural Networks optimizer based on Self-adaptive Differential Evolution is presented. This optimizer applies mutation and crossover operators in a new way, taking into account the structure of the network according to a per layer strategy. Moreover, a new crossover called interm is proposed, and a new self-adaptive version of DE called MAB-ShaDE is suggested to reduce the number of parameters. The framework has been tested on some well-known classification problems and a comparative study on the various combinations of self-adaptive methods, mutation, and crossover operators available in literature is performed. Experimental results show that DENN reaches good performances in terms of accuracy, better than or at least comparable with those obtained by backpropagation.
This paper presents an approach to artificial intelligence planning based on linear temporal logic (LTL). A simple and easy-to-use planning language is described, PDDL-K (Planning Domain Description Language with control Knowledge), which allows one to specify a planning problem together with heuristic information that can be of help for both pruning the search space and finding better quality plans. The semantics of the language is given in terms of a translation into a set of LTL formulae. Planning is then reduced to "executing" the LTL encoding, i.e. to model search in LTL. The feasibility of the approach has been successfully tested by means of the system Pdk, an implementation of the proposed method.
The diffusion of domotic and ambient intelligence systems have introduced a new vision in which autonomous deliberative agents operate in environments where reactive responses of devices can be cooperatively exploited to fulfill the agent's goals. In this article a model for automated planning in reactive environments, based on numerical planning, is introduced. A planner system, based on mixed integer linear programming techniques, which implements the model, is also presented. The planner is able to reason about the dynamic features of the environment and to produce solution plans, which take into account reactive devices and their causal relations with agent's goals by exploitation and avoidance techniques, to reach a given goal state. The introduction of reactive domains in planning poses some issues concerning reasoning patterns which are briefly depicted. Experiments of planning in reactive domains are also discussed.
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