We extend the 0-approximation of sensing actions and incomplete information in [Son and Baral 2000] to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with respect to this approximation is NP-complete. We then present an answer set programming based conditional planner, called ASCP, that is capable of generating both conformant plans and conditional plans in the presence of sensing actions, incomplete information about the initial state, and static causal laws. We prove the correctness of our implementation and argue that our planner is sound and complete with respect to the proposed approximation. Finally, we present experimental results comparing ASCP to other planners.

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.

This means that there is an issue over the best way of evaluating their effectiveness on both sound educational principles and on Human Computer Interface principles. It is the aim of this paper to highlight some of the steps to move toward an objective standard by which to gauge VLEs and ultimately to provide a single overall index measure (essentially a score out of 10) for both usability and educational worth based upon an analysis of accepted standards. An HCI index was constructed for general usability comparison and a separate educational index (EDI index) was designed to provide a measure of educational quality. First the Blackboard VLE and second an open source VLE, Moodle, were tested. As far as possible the open source VLE carried the same content as the Blackboard VLE to allow a comparison of the VLE structure and operation rather than its content. Usability statistics are obtained from a set of standard users.

Usually, probabilistic automata and probabilistic grammars have crisp symbols as inputs, which can be viewed as the formal models of computing with values. In this paper, we first introduce probabilistic automata and probabilistic grammars for computing with (some special) words in a probabilistic framework, where the words are interpreted as probabilistic distributions or possibility distributions over a set of crisp symbols. By probabilistic conditioning, we then establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling arbitrary inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with some special words. To compare the transition probabilities of two near inputs, we also examine some analytical properties of the transition probability functions of generalized extensions. Moreover, the retractions and the generalized extensions are shown to be equivalence-preserving. Finally, we clarify some relationships among the retractions, the generalized extensions, and the extensions studied recently by Qiu and Wang.

Through the Internet and the World-Wide Web, a vast number of information sources has become available, which offer information on various subjects by different providers, often in heterogeneous formats. This calls for tools and methods for building an advanced information-processing infrastructure. One issue in this area is the selection of suitable information sources in query answering. In this paper, we present a knowledge-based approach to this problem, in the setting where one among a set of information sources (prototypically, data repositories) should be selected for evaluating a user query. We use extended logic programs (ELPs) to represent rich descriptions of the information sources, an underlying domain theory, and user queries in a formal query language (here, XML-QL, but other languages can be handled as well). Moreover, we use ELPs for declarative query analysis and generation of a query description. Central to our approach are declarative source-selection programs, for which we define syntax and semantics. Due to the structured nature of the considered data items, the semantics of such programs must carefully respect implicit context information in source-selection rules, and furthermore combine it with possible user preferences. A prototype implementation of our approach has been realized exploiting the DLV KR system and its plp front-end for prioritized ELPs. We describe a representative example involving specific movie databases, and report about experimental results.

We investigate knowledge exchange among commercial organi sations, the rationale behind it and its effects on the marke t. Knowledge exchange is known to be beneficial for industry, bu t in order to explain it, authors have used high level concept s like network effects, reputation and trust. We attempt to formal ise a plausible and elegant explanation of how and why compan ies adopt information exchange and why it benefits the market as a whole when this happens. This explanation is based on a multi - agent model that simulates a market of software providers. E ven though the model does not include any high-level concept s, information exchange naturally emerges during simulation s as a successful profitable behaviour. The conclusions reac hed by this agent-based analysis are twofold: (1) A straightforward se t of assumptions is enough to give rise to exchange in a softwa re market. This work was carried out when M. Chli and P . The growth of the Internet as a medium of knowledge exchange has stimulated a lot of scientific interest origina ting from various disciplines. The willingness of individua ls, organisations as well as commercial firms to share information via the Internet has been remarkable. In some sectors like scientific research, the communication of newly acquir ed knowledge and expertise in a field is considered vital for the ir advancement. On the other hand, in other sectors, the benefit s of such exchanges may not be obvious. For instance, it might even be considered damaging for pharmaceutical companies t o make public any innovations generated by their Research and Development (R&D) process. In spite of this view, exchange o f intellectual property in some industries occurs quite freq uently and in various different ways. These include the forming of strategic partnerships, the participation in open source s oftware projects and the publication of scientific papers by researc h labs that are part of commercial companies. W e study the knowledge exchange that occurs in the software industry. In particular, we focus on analysing the rationale behind this exchange as well as its effect on the industry. The complexity of software requirements is a char - acteristic that distinguishes the software market from oth ers.

In this article we investigate a way in which quantum computing can be used to extend the class of fuzzy sets. The core idea is to see states of a quantum register as characteristic functions of quantum fuzzy subsets of a given set. As the real unit interval is embedded in the Bloch sphere, every fuzzy set is automatically a quantum fuzzy set. However, a generic quantum fuzzy set can be seen as a (possibly entangled) superposition of many fuzzy sets at once, offering new opportunities for modeling uncertainty. After introducing the main framework of quantum fuzzy set theory, we analyze the standard operations of fuzzification and defuzzification from our viewpoint. We conclude this preliminary paper with a list of possible applications of quantum fuzzy sets to pattern recognition, as well as future directions of pure research in quantum fuzzy set theory.

The adaptation rule for Vector Quantization algorithms, and consequently the convergence of the generated sequence, depends on the existence and properties of a function called the energy function, defined on a topological manifold. Our aim is to investigate the conditions of existence of such a function for a class of algorithms examplified by the initial ''K-means'' and Kohonen algorithms. The results presented here supplement previous studies and show that the energy function is not always a potential but at least the uniform limit of a series of potential functions which we call a pseudo-potential. Our work also shows that a large number of existing vector quantization algorithms developped by the Artificial Neural Networks community fall into this category. The framework we define opens the way to study the convergence of all the corresponding adaptation rules at once, and a theorem gives promising insights in that direction. We also demonstrate that the ''K-means'' energy function is a pseudo-potential but not a potential in general. Consequently, the energy function associated to the ''Neural-Gas'' is not a potential in general.

Department of Mathematics, University of New Mexico, Gallu p, NM 87301, U.S.A. Abstract: This paper presents two new promising combination rules for the fusion of uncertain and potentially highl y conflicting sources of evidences in the theory of belief func - tions established first in Dempster-Shafer Theory (DST) and then recently extended in Dezert-Smarandache Theory (DSmT). Our work is to provide here new issues to palliate the well-known limitations of Dempster's rule and to work beyond its limits of applicability. Since the famous Zadeh' s criticism of Dempster's rule in 1979, many researchers have proposed new interesting alternative rules of combination to palliate the weakness of Dempster's rule in order to provide acceptable results specially in highly conflicting situati ons. Bot h rules allow to deal with highly conflicting sources for stati c and dynamic fusion applications. W e present some interesting properties for ACR and PCR rules and discuss some simulation results obtained with both rules for Zadeh's pro b-lem and for a target identification problem.

In this short paper we present a linear constraint solver for the UniCalc system, an environment for reliable solution of mathematical modeling problems.