An extension of the pseudobayesian model by making use of Dempster-Shafer's theory
Analogie - užitečná heuristická strategie
This article describes a heuristic strategy of analogy and illustrates it by five school mathematics examples. These examples are efficiently solved while using such a strategy.
Analysis of problem solving process of a mental rotations test. Performance in Shepard-Metzler tasks.
Analýza problémov lokálneho prehľadávania a ich heuristík
Ant algorithm for grid scheduling powered by local search.
Ant-based extraction of rules in simple decision systems over ontological graphs
In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values...
Approximate Model Checking of Real-Time Systems for Linear Duration Invariants
Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented....
Approximation of proximities by aggregating T-indistinguishability operators.
Automatic Video Annotation with Forests of Fuzzy Decision Trees.
Belief functions induced by multimodal probability density functions, an application to the search and rescue problem
In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...
Belief functions induced by multimodal probability density functions, an application to the search and rescue problem
In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two...
Bi-directional nearness in a network by AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process)
Bi-directional nearness in a network by AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process)
In this paper we study bi-directional nearness in a network based on AHP (Analytic Hierarchy Process) and ANP (Analytic Network Process). Usually we use forward (one-dimensional) direction nearness based on Euclidean distance. Even if the nearest point to i is point j, the nearest point to j is not necessarily point i. Sowe propose the concept of bi-directional nearness defined by AHP'ssynthesizing of weights “for” direction and “from” direction. This concept of distance is a relative distance...
Bounds of graph parameters for global constraints
This article presents a basic scheme for deriving systematically a filtering algorithm from the graph properties based representation of global constraints. This scheme is based on the bounds of the graph parameters used in the description of a global constraint. The article provides bounds for the most common used graph parameters.
Bounds of graph parameters for global constraints
This article presents a basic scheme for deriving systematically a filtering algorithm from the graph properties based representation of global constraints. This scheme is based on the bounds of the graph parameters used in the description of a global constraint. The article provides bounds for the most common used graph parameters.
Building adaptive tests using Bayesian networks
We propose a framework for building decision strategies using Bayesian network models and discuss its application to adaptive testing. Dynamic programming and algorithm are used to find optimal adaptive tests. The proposed algorithm is based on a new admissible heuristic function.
Capital budgeting problems with fuzzy cash flows.
We consider the internal rate of return (IRR) decision rule in capital budgeting problems with fuzzy cash flows. The possibility distribution of the IRR at any r ≥ 0, is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor r equals to zero. Generalizing our earlier results on fuzzy capital budegeting problems [Car99] we show that the possibility distribution of the {IRR} is a highly nonlinear function which is getting more and more unbalanced...
Case-based reasoning framework for generating decision support systems.
Combination of mobile agent and evolutionary algorithm to optimize the client transport services
This paper presents a migration strategy for a set of mobile agents (MAs) in order to satisfy customers' requests in a transport network, through a multimodal information system. In this context, we propose an optimization solution which operates on two levels. The first one aims to constitute a set of MAs building their routes, called Workplans. At this level, Workplans must incorporate all nodes, representing information providers in the multimodal network, in order to explore it completely....
Combining constraint Propagation and meta-heuristics for searching a Maximum Weight Hamiltonian Chain
This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy...