In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which multiple users have distinct preferences. However, making suitable decisions requires that the preferences of a specific user for different configurations be articulated or elicited, something generally acknowledged to be onerous. We address two problems associated with prefere...

In this paper, multi-criterion, evolutionary and quantum decision making supported by the Adaptive Quantum-based Multi-criterion Evolutionary Algorithm (AQMEA) has been considered. AQMEA has been developed to the task assignment problem and to underwater vehicle planning. Moreover, the other algorithms like QMEA and QEA have been discussed. Key-Words: quantum algorithms, decision making, multi-...

The aim of this work is to compare different approaches for parallelization in model-based optimization. As another alternative aside from the existing methods, we propose using a multi-objective infill criterion that rewards both the diversity and the expected improvement of the proposed points. This criterion can be applied more universally than the existing ones because it has less requireme...

Qualitative decision tools have been used in AI and CS in various contexts, but their adequacyis still unclear. To examine this question, our work employs the axiomatic approach to characterize the properties of various decision rules. In the past, we presented a constructive representation theorem for the maximin decision criterion, and we characterized conditions under which an agent can be v...

The paper describes a new preference method and its use in multiobjective optimisation. These preferences are developed with a goal to reduce the cognitive overload associated with the relative importance of a certain criterion within a multiobjective design environment involving large numbers of objectives. Their successful integration with several genetic algorithm–based design search and opt...

In this paper, we consider discrete-time partially observed mean-field games with the risk-sensitive optimality criterion. We introduce risk-sensitivity behavior for each agent via an exponential utility function. game model, is weakly coupled rest of population through its individual cost and state dynamics empirical distribution states. establish equilibrium in infinite-population limit using...

Probability updating via Bayes' rule often entails extensive informational and computational requirements. In consequence, relatively few practical applications of Bayesian adaptive control techniques have been attempted. This paper discusses an alternative approach to adaptive control, Bayesian in spirit, which shifts attention from the updating of probability distributions via transitional pr...

In interactive multi-objective reinforcement learning (MORL), an agent has to simultaneously learn about the environment and preferences of user, in order quickly zoom on those decisions that are likely be preferred by user. this paper we study MORL context multi-armed bandits. Contrary earlier approaches force utility user expressed as a weighted sum values for each objective, do not make such...

A counterpart to von Neumann and Morgenstern' expected utility theory is proposed in the framework of possibility theory. The existence of a utility function, representing a preference ordering among possibility distributions (on the consequences of decision-maker's actions) that satisfies a series of axioms pertaining to decision-maker's behavior, is established. The obtained utility is a gene...

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets...