Valuable stimuli are invariably localized in space. While our knowledge regarding the neural networks supporting value assignment and comparisons is considerable, we lack a basic understanding of how the human brain integrates motivational and spatial information. The amygdala is a key structure for learning and maintaining the value of sensory stimuli and a recent non-human primate study provi...

Decision Analysis and Decision Support is an area in which applications of fuzzy set theory can be found since the early 70’s. Still there are areas, such as fuzzy controls, that have gained much wider acceptance in practice than fuzzy decision analysis. This paper tries to describe where fuzzy decision support stands now and what would have to be done to gain wider acceptance. One of the major...

Learning of autonomous agents during their interaction with the environment is one of the frequent tasks in machine learning. However, the efficiency of the learning process drops dramatically with the complexity of the problem domain. In this paper, we present a survey of existing methods for reduction of the dimensionality. These methods can help find efficient solutions to the reinforcement ...

In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models estimated from data collected from the animal or herd. State space models (SSMs) are a general tool for mo...

REGULAR PAPERS An Improved Image Segmentation Algorithm Based on GPU Parallel Computing Haiyang Li, Zhaofeng Yang, and Hongzhou He Privacy-Preserving Location Assurance Protocols for Mobile Applications Genqiang Wu, Yeping He, Yi Lu, and Liping Ding Conversion of Sign Language to Spoken Sentences by Means of a Sensory Glove Pietro Cavallo and Giovanni Saggio Analysis of a Multivariate Public Ke...

This paper describes a part of speech tagger. The tagger is based on a set of probability mixture models. Each mixture model is responsible for tagging of a specific class of words, sharing similar context properties. Probability mixture models contain 25 various mixture components. The tagger is tested on Polish language and compared to other available taggers.

This paper examines a decision-tree framework for instance-space decomposition. According to the framework, the original instance-space is hierarchically partitioned into multiple subspaces and a distinct classifier is assigned to each subspace. Subsequently, an unlabeled, previously-unseen instance is classified by employing the classifier that was assigned to the subspace to which the instanc...

In this paper we embed m-dimensional Euclidean space in the geometric algebra Clm to extend the operators of incidence in R to operators of incidence in the geometric algebra to generalize the notion of separator to a decision boundary hyperconic in the Clifford algebra of hyperconic sections denoted as Cl(V2). This allows us to extend the concept of a linear perceptron or the spherical percept...

In this paper, we use normal surface theory to study Dehn filling on a knot-manifold. First, it is shown that there is a finite computable set of slopes on the boundary of a knot-manifold that bound normal and almost normal surfaces in a one-vertex triangulation of that knot-manifold. This is combined with existence theorems for normal and almost normal surfaces to construct algorithms to deter...

In this paper we present duplication-free tableau calculi for three propositional intermediate interpolable logics, namely the logic characterized by rooted Kripke models with depth two at most, the logic characterized by rooted Kripke models with two final elements at most and depth two at most and the logic characterized by rooted Kripke models with a final element at most (also known as Jank...