This paper presents a decentralized method for computing Pareto-optimal solutions in multiparty negotiations over continuous issues. The method is based on the well known weighting method which is decomposed by introducing an own decision variable for each decision maker and by applying the dual decomposition method to the resulting problem. The method o€ers a systematic way for generating some...

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable pr...

Evaluating associations and relationships between variable is a very challenging and important problem in the domain of medicine and clinical data analysis. Not many classification methods have been tried in the literature to tackle this problem. Decision Tree is one of the most important data mining methods that can be used, due to their property of implicitly performing variable screening or ...

The Recognition Heuristic (Gigerenzer & Goldstein, 1996; Goldstein & Gigerenzer, 2002) makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision s...

Reinforcement learning agents attempt to learn and construct a decision policy which maximises some reward signal. In turn, this policy is directly derived from long-term value estimates of state-action pairs. In environments with real-valued state-spaces, however, it is impossible to enumerate the value of every state-action pair, necessitating the use of a function approximator in order to in...

We investigate a new class of metrics to find good variable orders for decision diagrams in symbolic state-space generation. Most of the previous work on static ordering is centered around the concept of minimum variable span, which can also be found in the literature under several other names. We use a similar concept, but applied to event span, and generalize it to a family of metrics paramet...

Ordered Binary Decision Diagrams (OBDDs) are the rst choice in manipulating and representing Boolean functions in CAD. Since the size of an OBDD heavily depends on the chosen variable order, much eeort is spent in nding good and improving existing variable orders. If these optimizing techniques are used in OBDD applications, one has to cope with OBDDs of diier-ent variable orders very often (e....

In engineering systems, noise is a curse, obscuring important signals and increasing the uncertainty associated with measurement. However, the negative effects of noise and uncertainty are not universal. In this paper, we examine how people learn sequential control strategies given different sources and amounts of feedback variability. In particular, we consider people's behavior in a task wher...

Fault tree analysis is a commonly used technique to assess the systems reliability performance in terms of its components reliability characteristics. More recently, the Binary Decision Diagram (BDD) methodology has been introduced which significantly aids the analysis of the fault tree diagram. The approach has been shown to improve both the efficiency of determining the minimal cut sets of th...

Classical economic theory following Bentham and Mill assumes decision makers are completely informed, highly sensitive to differences between alternatives, and above all rational in being able to rank order the possible alternatives and choose among them on some measure of welfare (utility maximization). In recent years, this view has been successfully challenged by empirical studies of decisio...