We propose a new class of quantum computing algorithms which generalize many standard ones. The goal of our algorithms is to estimate probability distributions. Such estimates are useful in, for example, applications of Decision Theory and Artificial Intelligence, where inferences are made based on uncertain knowledge. The class of algorithms that we propose is based on a construction method th...

This paper tries to discuss some findings in mathematical decision-making modeling models with applications in business processes. We start by presenting some technological implications and implementations of decision-making models. After this we discuss some implementations realized by us and that consists in a neural network, a JAVA implementation of the decision-making model, an expert syste...

CR-Prolog is an extension of the knowledge representation language A-Prolog. The extension is built around the introduction of consistency-restoring rules (cr-rules for short), and allows an elegant formalization of events or exceptions that are unlikely, unusual, or undesired. The flexibility of the language has been extensively demonstrated in the literature, with examples that include planni...

Potential field methods are rapidly gaining popularity in obstacle avoidance applications for mobile robots. The general principles of potential field path planning are presented first. The electrostatic potential field (EPF) path planner is then combined with a twolayered fuzzy logic inference engine and implemented for mobile robot navigation in a 2-D dynamic environment. The first layer of t...

Rule based inference has demonstrated its applicability for a wide variety of domains. As users have grown more ~omfort­ able with this technology, the scope of attempted proJects has grown from small laboratory demonstrations into massive real world time-critical systems. However, as the scope of systems has increased, execution speed has become u~acceptabl~. <?ne method of improving inference...

The usual, comparative, conception of Inference to the Best Explanation (IBE) takes it to be ampliative. In this paper I propose a conception of IBE (‘Holmesian inference’) that takes it to be a species of eliminative induction and hence not ampliative. This avoids several problems for comparative IBE (e.g. how could it be reliable enough to generate knowledge?). My account of Holmesian inferen...

A common computational goal is to encapsulate the modeling of a target phenomenon within a unified and comprehensive ”engine”, which addresses a broad range of the required processing tasks. This goal is followed in common modeling of the morphological and syntactic levels of natural language, where most processing tasks are encapsulated within morphological analyzers and syntactic parsers. In ...

We are developing a geometric inference system on Internet. A rough geometric figure drawn by a student is transformed into an ideal one that satisfies the assumptions. The system extracts particular patterns in this figure and tries to connect the assumptions and the conclusion. The present paper extends the visualization of the proof by introducing a multiple view which consists of (a) senten...

This paper introduces the Bayesian Inference Engine (BIE), a general parallel-optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. I describe key concepts that illustrate the power of Bayesian inference to address these needs and outline th...