38658502
Learning by Discovering Problem Solving Heuristics Through Experience
The authors present a system, called SAFE (strategy acquisition from experience), which incorporates novel methods for discovering domain-dependent problem-solving heuristics. SAFE is implemented as a sorting system whose sorting strategies are represented as production rules. SAFE initially uses the insertion sort strategy to solve problems. After solving each given problem. SAFE learns symbolic rules from the solution path which is obtained by applying the existing heuristic information. By one or several processes of learning. SAFE is able to obtain the heuristics to sort new problems with minimum exchanges of elements. The notion of shortcut, an effective inductive learning bias for reducing the hypothesis space to be searched during learning, is introduced. >
27866706
A New Method of Circumscribing Beliefs: The Propositional Case
We propose here a new notion of a minimal model for forming circumscription in belief-logic by incorporating the notion of minimal models in the prepositional logic. This results in a new circumscription operation for belief-formulas than the one considered in [4-6]. An important feature of the new circumscription operation is its close relationship with the circumscription of prepositional formulas. For instance, we have CIRC[Bp] = B(CIRC[p]), where p is a prepositional formula. We give several examples to show that the new notion of circumscription fits quite well with intuition.
14245135
Automatic Correlation and Calibration of Noisy Sensor Readings Using Elite Genetic Algorithms
This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray scale images corrupted with noise. Both tabu search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results.
30548729
A Class of Stratified Programs in Autoepistemic Logic of Knowledge and Belief
Przymusinski proposed [11] AELB, the autoepistemic logic of knowledge and belief. The AELB logic is a very powerful and expressive formal system which isomorphically contains various major non-monotonic knowledge representation formalisms and major logic program semantics. Thus AELB can be used as a unifying framework for non-monotonic knowledge representation formalisms, which allows us to compare and better understand different formalizations of common-sense knowledge.
9144862
Rules and fuzzy rules in text: concept, extraction and usage
Several concepts and techniques have been imported from other disciplines such as Machine Learning and Artificial Intelligence to the field of textual data. In this paper, we focus on the concept of rule and the management of uncertainty in text applications. The different structures considered for the construction of the rules, the extraction of the knowledge base and the applications and usage of these rules are detailed. We include a review of the most relevant works of the different types of rules based on their representation and their application to most of the common tasks of Information Retrieval such as categorization, indexing and classification.
39684197
A sound and complete proof theory for the generalized logic of only knowing
We present a sound and complete proof theory for the generalized logic of only knowing (GOL). The GOL logic is a modal logic proposed recently (Chen 1997) as a unified logical framework for non-monotonic reasoning, which extends the logic of only knowing (OL) of Levesque (1990), and covers the important notion of epistemic specification (Gelfond 1991, Gelfond and Przymusinska 1993) which is very useful for knowledge representation. The OL logic has been shown to have close connections with Lifschitz's minimal belief and negation as failure (Lifschitz 1994) and various other major non-monotonic formalisms. The GOL logic is more general and powerful than OL. The completeness result for the GOL proof theory establishes the theoretical foundation for answering queries in epistemic theories. This work is restricted to the propositional case only.
16331940
Optimal Parallel Routing in Star Networks
Star networks have recently been proposed as attractive alternatives to the popular hypercube for interconnecting processors on a parallel computer. In this paper, we present an efficient algorithm that constructs an optimal parallel routing in star networks. Our result improves previous results for the problem.
691108
Utilization-based admission control for real-time applications
In this paper, we present a methodology to use utilization-based admission control in guaranteed real-time communication in a scalable fashion. We make admission control scalable by using a configuration-time test to determine a safe utilization level of servers. Admission control at run-time then is reduced to simple utilization tests on the servers along the path of the new flow. Furthermore, we discuss how appropriate route selection improve utilization levels, design a safe route selection heuristic algorithm to achieve high utilization of resources, and derive two bounds on the maximum utilization level for given traffic in a network. We compare the results of our route selection heuristics with that of a shortest-path based algorithm, and find that our heuristics can achieve a much higher maximum utilization level than that of the shortest-path based algorithm.
43039500
A Linear Time Algorithm for Isomorphism of Graphs of Bounded Average Genus
Recent progress in topological graph theory has shown potential applicability of average genus to graph isomorphism testing. The present paper describes an initial effort at combining topological invariants with combinatorial analysis to design efficient graph isomorphism algorithms. In particular, a linear time algorithm for isomorphism of graphs of bounded average genus is presented.
18417913
IT induction in the food service industry
As part of the business strategy in a competitive environment, for an organization to induct information technology (IT) appropriately, business process reengineering (BPR) must be conducted. We propose a process that incorporates both management information systems and strategic management concepts for the IT induction. First, we develop a business competition strategy based on Porter’s five competitive forces; second, using concepts developed in the field of information systems the business processes are reengineered to improve effectiveness and efficiency; and finally, IT strategy is developed to achieve these goals. A real‐life case in the food service industry is used to illustrate this process.
28375225
User-centered indexing for adaptive information access
We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Learning and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.