Integrating integer programming and probabilistic deduction graphs for probabilistic reasoning

Optimal solutions of several variants of the probabilistic reasoning problem were found by a new technique that integrates integer programming and probabilistic deduction graphs (PDG). PDGs are extended from deduction graphs of the and-type via normal deduction graphs. The foregoing variants to be solved can involve multiple hypotheses and multiple evidences where the former is given and the latter is unknown and being found or vice versa. The relationship among these hypotheses and evidences with possible intermediaries is represented by a causal graph. The proposed method can handle a large causal graph of any type and find an optimal solution by invoking a linear integer programming package. In addition, formulating the reasoning problem to fit integer programming takes a polynomial time. H.-L. Li was visiting the Department of Computer Sciences, University of North Texas in 1988–1989. He is with the Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan, R.O.C.     

Granular Reasoning Using Zooming In & Out: Part 2. Aristotle's Categorical Syllogism

The concept of granular computing is applied to Aristotle's categorical syllogism. Such kind of reasoning is called granular reasoning in this paper. For the purpose, two operations called zooming in & out are introduced to reconstruct granules of possible worlds.     

Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

On a responsive replenishment system: a fuzzy logic approach

In today's competitive business environment, it is important that customers are able to obtain their preferred items in the shops they visit, particularly for convenience store chains such as 7–Eleven where popular items are expected to be readily available on the shelves of the stores for buyers. To minimize the cost of running such store chains, it is essential that stocks be kept to a minimum and at the same time large varieties of popular items are available for customers. In this respect, the replenishment system needs to be able to cope with the taxing demands of minimal inventory but at the same time keeping large varieties of needed items. This paper proposes a replenishment system which is able to respond to the fluctuating demands of customers and provide a timely supply of needed items in a cost–effective way. The proposed system embraces the principle of fuzzy logic which is able to deal with uncertainties by virtue of its fuzzy rules reasoning mechanism, thereby leveraging the responsiveness of the entire replenishment system for the chain stores. To validate the feasibility of the approach, a case study has been conducted in an emulated environment with promising results.     

Evaluating an intelligent diagnosis system of historical text comprehension

This work aims to present and evaluate a Fuzzy-Case Based Reasoning Diagnosis system of Historical Text Comprehension. The synergism of fuzzy logic and case based reasoning techniques handles the uncertainty in the acquisition of human expert's knowledge regarding learner's observable behaviour and integrates the right balance between expert's knowledge described in the form of fuzzy sets and previous experiences documented in the form of cases. The formative evaluation focused on the comparison of the system's performance to the performance of human experts concerning the diagnosis accuracy. The system was also evaluated for its behaviour when using two different historical texts. Empirical evaluation conducted with human experts and real students indicated the need for revision of the diagnosis model. The evaluation results are encouraging for the system's educational impact on learners and for future work concerning an intelligent educational system for individualized learning.     

Powers of observation in creative design

Being perceptive is a trait highly valued in scientific and engineering professions. What a scientist or engineer notices while considering a problem, evaluating alternatives, or interpreting data has a profound impact on how a problem is viewed and solved. This paper focuses on processes we believe underlie being perceptive: firstly, preparation—becoming attuned to salient or important features; secondly, assimilation-detection and exploration of patterns (invariants) as well as anomalies; and thirdly, strategic control-heuristic strategies for exploring the implications of what has been observed. These processes play an integral role in characteristic activities within creative design, including problem reformulation, the emergence of properties and constraints on the solution, and the ability to incorporate into the design experimental feedback from the environment and from experiences with prototypes and previous designs. The paper presents a computational model incorporating these ideas, implemented in a system called IMPROVISER.     

Automated Theorem Proving in Temporal Logic:T-Resolution

This paper presentes a novel resolution method,T-resolution,based on the first order temporal logic.The primary claim of this method is its soundness and completeness.For this purpose,we construct the corresponding semantic trees and extend Herbrand‘s Theorem.     

How to make and defend a proposal in a deliberation dialogue

In this paper it is shown how tools developed in argumentation theory and artificial intelligence can be applied to the development of a new dialectical analysis of the speech act of making a proposal in a deliberation dialogue. These tools are developed, modified and used to formulate dialogue pre-conditions, defining conditions and post-conditions for the speech act of making a proposal in a deliberation dialogue. The defining conditions set out what is required for a move in a dialogue to count as the making of a proposal by one of the parties. What is required are the conditions that (1) the move fit the requirements of the argumentation scheme for practical reasoning, and (2) the premises are propositions describing common goals of both parties or propositions that they reasonably consider means to achieve these goals. The analysis goes beyond the standard speech act approach by specifying not only the normative requirements for making a well-formed proposal, but also the requirements for responding to it by questioning or criticizing it, and the requirements for defending it.     

Branching and pruning: An optimal temporal POCL planner based on constraint programming

A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alternative branching scheme but lacking comparable pruning mechanisms do not perform as well. In this paper, a domain-independent formulation of temporal planning based on Constraint Programming is introduced that successfully combines a POCL branching scheme with powerful and sound pruning rules. The key novelty in the formulation is the ability to reason about supports, precedences, and causal links involving actions that are not in the plan. Experiments over a wide range of benchmarks show that the resulting optimal temporal planner is much faster than current ones and is competitive with the best parallel planners in the special case in which actions have all the same duration.¹