No abstract

No abstract

No abstract

No abstract

Emotional affordances represent a recently introduced concept which model all the mechanisms used to collect/transmit emotional meaning in the context of human machine interaction. In this work, we introduce and formally define the cognitive role of emotional affordances in a collaboration human-machine dialogue as tools for triggering or recognizing planning-based activities of delegation, goal negotiation, state acquisition, plan prioritization, taking place with the interaction partner. The presented formal model is grounded in an emergency scenario where reacting to emotional affordances or transmitting an emotional content is instrumental to reach the goal of an effective collaborative response. The implementation issues of generation and recognition of emotional affordance are also discussed

No abstract

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.

No abstract