A fuzzy approach to analogical reasoning

 We address the problem of the representation of resemblances involved in analogical reasoning. We use fuzzy relations to compare situations. We provide constructive methods to adapt the solution of an already solved situation to a similar new situation according to the degree of resemblance between these two situations. We give a general definition of analogical scheme which can be considered from a more or less constrained point of view.     

A logical approach to representing and reasoning about space

The need for a formal language in which to express and reason about spatial concepts is of crucial importance in many areas of AI and visual systems. For the last five years, spatial reasoning research by the Qualitative Spatial Reasoning Group, University of Leeds, has centred on the development and application of such a language — the RCC spatial logic. Below, we briefly describe the work of the group in this area.     

A virtual prototyping approach to product disassembly reasoning

An important aspect of Design for the Life Cycle is assessing the disassemblability of products. After an artifact has completed its useful life, it must be disassembled then recycled, remanufactured or scrapped. Disassemblability of a product can be evaluated by performing disassembly activities on prototypes. Virtual prototyping (VP) is an alternative to hardware prototyping in which analysis of designs can be done without manufacturing physical samples. In recent years, disassembly processes have been generated either by using interactive or automated approaches, but these approaches have limitations. Interactive approaches require extensive user input usually in the form of answering questions, whereas automated approaches can only be used to generate disassembly processes for products with simple component configuration and geometry. In this paper automated and interactive techniques are combined, using VP, to generate complete disassembly processes of a product design. To support generation of disassembly processes of a product, a virtual environment and VP method were developed that will support disassembly activities performed by a designer. The product model of the virtual prototype is generated from the CAD model. The disassembly process model for the prototype is generated using automated reasoning techniques and is completed by interactively disassembling the product in the virtual environment. Extensions to automatic reasoning techniques to compute ranges of feasible directions of component removal were developed to facilitate the generation of the disassembly process. A scheme to represent the disassembly process for disassemblability evaluation was developed and implemented. In this paper a Chrysler LHS center console has been used to illustrate our approach of generating disassembly processes via VP.     

Computing the minimal relations in point-based qualitative temporal reasoning through metagraph closure

Computing the minimal representation of a given set of constraints (a CSP) over the Point Algebra (PA) is a fundamental temporal reasoning problem. The main property of a minimal CSP over PA is that the strongest entailed relation between any pair of variables in the CSP can be derived in constant time. We study some new methods for solving this problem which exploit and extend two prominent graph-based representations of a CSP over PA: the timegraph and the series-parallel (SP) metagraph. Essentially, these are graphs partitioned into sets of chains and series-parallel subgraphs, respectively, on which the search is supported by a metagraph data structure. The proposed approach is based on computing the metagraph closure for these representations, which can be accomplished by some methods studied in the paper.In comparison with the known techniques based on enforcing path consistency, under certain conditions about the structure of the input CSP and the size of the generated metagraph, the proposed metagraph closure approach has better worst-case time and space complexity. Moreover, for every sparse CSP over the convex PA, the time complexity is reduced to O(n²) from O(n³), where n is the number of variables involved in the CSP.An extensive experimental analysis presented in the paper compares the proposed techniques and other known algorithms. These experimental results identify the best performing methods and show that, in practice, for CSPs exhibiting chain or SP-graph structure and randomly generated (both sparse and dense) CSPs, the metagraph closure approach is significantly faster than the approach based on enforcing path consistency.     

A constraint-based approach to spatiotemporal reasoning

We introduce a form of spatiotemporal reasoning that uses homogeneous representations of time and the three dimensions of space. The basis of our approach is Allen's temporal logic on the one hand and general constraint satisfaction algorithms on the other, where we present a new view of constraint reasoning to cope with the affordances of spatiotemporal reasoning as introduced here. As a realization for constraint reasoning, we suggest a massively parallel implementation in form of Boltzmann machines.     

A causal approach to nonmonotonic reasoning

We introduce logical formalisms of production and causal inference relations based on input/output logics of Makinson and Van der Torre [J. Philos. Logic 29 (2000) 383–408]. These inference relations will be assigned, however, both standard semantics (giving interpretation to their rules), and natural nonmonotonic semantics based on the principle of explanation closure. The resulting nonmonotonic formalisms will be shown to provide a logical representation of abductive reasoning, and a complete characterization of causal nonmonotonic reasoning from McCain and Turner [Proc. AAAI-97, Providence, RI, 1997, pp. 460–465]. The results of the study suggest production and causal inference as general nonmonotonic formalisms providing an alternative representation for a significant part of nonmonotonic reasoning.     

A case-based reasoning approach to planning for disassembly

With recycling regulations, resource conservation needs and an increased awareness of the state of the environment by both the consumer and the producer, many companies are establishing disassembly plants and developing product designs that specifically facilitate disassembly. Once disassembled, the items can be reused, recycled or discarded. One can identify two distinct aspects of the disassembly problem: design for disassembly (DFD) and planning for disassembly (PFD). The goal of DFD is to design products that are easy to disassemble. On the other hand, the objective of PFD is to identify efficient sequences to disassemble products. This paper focuses on the PFD aspect of disassembly. Because there could be many ways to disassemble a given product, PFD knowledge is accumulated by experience. Such knowledge is valuable, and should be captured, saved and reused to solve similar problems that arise in the future. In this paper, we propose case-based reasoning (CBR) as an approach to solve PFD problems. CBR is based on the fundamental principle that problem solving can benefit from solutions to past problems that have been attempted. The technique and issues related to the application of CBR to PFD are presented.     

A relational approach to support software architecture analysis

This paper reports on our experience with a relational approach to support the analysis of existing software architectures. The analysis options provide for visualization and view calculation. The approach has been applied for reverse engineering. It is also possible to check concrete designs against architecture-related rules. The paper surveys the theory, the tools and some of the applications developed so far. © 1998 John Wiley & Sons, Ltd.     

A case-based reasoning approach to cognitive map-driven tacitknowledge management

Knowledge is at the heart of knowledge management. In literature, a lot of studies have been suggested covering the role of knowledge in improving the performance of management. However, there are few studies about investigating knowledge itself in the arena of knowledge management. Knowledge circulating in an organization may be explicit or tacit. Until now, literature in knowledge management shows that it has mainly focused on explicit knowledge. On the other hand, tacit knowledge plays an important role in the success of knowledge management. It is relatively hard to formalize and reuse tacit knowledge. Therefore, research proposing the explication and reuse of tacit knowledge would contribute significantly to knowledge management research. In this sense, we propose using cognitive map (CM) as a main vehicle of formalizing tacit knowledge, and case-based reasoning as a tool for storing CM-driven tacit knowledge in the form of frame-typed cases, and retrieving appropriate tacit knowledge from the case base according to a new problem. Our proposed methodology was applied to a credit analysis problem in which decision-makers need tacit knowledge to assess whether a firm under consideration is healthy or not. Experiment results showed that our methodology for tacit knowledge management can provide decision makers with robust knowledge-based support.     

A rough set approach to reasoning under uncertainty

Abstract

Reasoning with uncertain information is a problem of key importance when dealing with information about the real world. Obtaining the precise numbers required by many uncertainty handling formalisms can be a problem. The theory of rough sets makes it possible to handle uncertainty without the need for precise numbers, and so has some advantages in such situations. This paper presents an introduction to various forms of reasoning under uncertainty that are based on rough sets. In particular, a number of sets of numerical and symbolic truth values which may be used to augment propositional logic are developed, and a semantics for these values is provided based upon the notion of possible worlds. Methods of combining the truth values are developed so that they may be propagated when augmented logic formulae are combined, and their use is demonstrated in theorem proving.     

A linear programming approach to reasoning about probabilities

We continue our study, initiated in [9], of the following computational problem proposed by Nilsson: Several clauses (Boolean functions of several variables) are given, and for each clause the probability that the clause is true is specified. We are asked whether these probabilities are consistent. They are if there is a probability distribution on the truth assignments such that the probability of each clause is the measure of its satisfying set of assignments. Since this is a generalization of the satisfiability problem of predicate calculus, it is immediately NP-hard. In [9] we showed certain restricted cases of the problem to be NP-complete, and used the Ellipsoid Algorithm to show that a certain special case is in P. In this paper we use the Simplex method, column generation techniques, and variable-depth local search to derive an effective heuristic for the general problem. Experiments show that our heuristic performs successfully on instances with many dozens of variables and clauses. We also prove several interesting complexity results that answer open questions in [9] and motivate our approach.     

A multi-viewpoint system to support abductive reasoning

The paper proposes a multi-viewpoint system to support human abductive reasoning for diagnosis, prognosis and trial-and-error activities for supervising automated systems. This multi-viewpoint approach interprets the same set of events from the different viewpoints in this set. The algorithms for managing these viewpoints and the set of events are related to hypothetical reasoning, and they use several main functions to (1) select or reject certain events, (2) cancel or recover these events, and (3) manage the consistency of the viewpoints. This approach is applied to diagnosis and trial-and-error activities related to the phone troubleshooting problem.     

A novel approach to guarantee good robustness of fuzzy reasoning

The analysis of internal connective operators of fuzzy reasoning is very significant and the robustness of fuzzy reasoning has been calling for study. An interesting and important question is that, how to choose suitable internal connective operators to guarantee good robustness of rule-based fuzzy reasoning? This paper is intended to answer it. In this paper, Lipschitz aggregation property and copula characteristic of t-norms and implications are discussed. The robustness of rule-based fuzzy reasoning is investigated and the relationships among input perturbation, rule perturbation and output perturbation are presented. The suitable t-norm and implication can be chosen to satisfy the need of robustness of fuzzy reasoning. In 1-Lipschitz operators, if both t-norm and implication are copulas, the rule-based fuzzy reasoning is much more stable and more reliable. In copulas, if both t-norm and implication are 1-l_∞-Lipschitz, they can guarantee good robustness of fuzzy reasoning. The experiments not only illustrate the ideas proposed in the paper but also can be regarded as applications of soft computing. The approach in the paper also provides guidance for choosing suitable fuzzy connective operators and decision making application in rule-based fuzzy reasoning.     

An approach to fuzzy default reasoning for function approximation

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First, we briefly describe existing approaches to the handling of default rules in the framework of possibility theory. Next, we show that standard interpolation-based fuzzy reasoning leads to counterintuitive results when general rules include specific rules with different consequents. Then, we demonstrate that intuitively acceptable results are obtained from a non-standard inclusion-based fuzzy reasoning method. Our approach is based on the preference for more specific rules, which is a commonly used idea in the field of default reasoning. When a general rule includes a specific rule and they are both compatible with an input vector, the weight of the general rule is discounted in fuzzy reasoning. We also discuss the case where general rules do not perfectly but partially include specific rules. Then we propose a genetics-based machine learning (GBML) algorithm for extracting a small number of fuzzy if-then rules with different specificity levels from numerical data using our inclusion-based fuzzy reasoning method. Finally, we describe how our approach can be applied to the approximate realization of fuzzy number-valued nonlinear functions     

A relational approach to defining and implementing transformations between metamodels

The Model-Driven Architecture initiative of the OMG promotes the idea of transformations in the context of mapping from platform independent to platform specific models. Additionally, the popularity of XML and the wide spread use of XSLT has raised the profile of model transformation as an important technique for computing. In fact, computing may well be moving to a new paradigm in which models are considered first class entities and transformations between them are a major function performed on those models. This paper proposes an approach to defining and implementing model transformations which uses metamodelling patterns to capture the essence of mathematical relations. It shows how these patterns can be used to define the relationship between two different metamodels. A goal of the approach is to enable complete specifications from which tools can be generated. The paper describes implementations of the examples, which have been partially generated from the definitions using a tool generation tool. A number of issues emerge which need to be solved in order to achieve the stated goal; these are discussed.     

Default reasoning and belief revision: A syntax-independent approach

As an important variant of Reiter‘s default logic.Poole(1988) developed a nonmonotonic reasoning framework in the classical first-order language,Brewka and Nebel extended Poole‘s approach in order to enable a representation of priorities between defaults.In this paper a general framework for default reasoning is presented,which can be viewed as a generalization of the three approaches above.It is proved that the syntax-independent default reasoning in this framework is identical to the general belief revision operation introduced by Zhang et al.(1997).This esult provides a solution to the problem whether there is a correspondence between belief revision and default logic for the infinite case .As a by-product,an answer to the the question,raised by Mankinson and Gaerdenfors(1991),is also given about whether there is a counterpart contraciton in nonmonotonic logic.     

Representing and matching simulation cases: A case-based reasoning approach

Simulation modelling is a complex decision-making process that involves the processing of various knowledge and information within a context defined by specific application. Building a “good” simulation model has been heavily reliant on the skill and experience of human expert, which has become one of the most expensive and limited resources in market competition. Case-based reasoning (CBR) can be used to effectively solve problems in ill-defined domains where operations specific knowledge and information are processed in a contextual manner such as simulation modeling. This paper addresses some of the basic issues in applying CBR to improve simulation modeling, with emphasis on knowledge or case representation, case indexing, and case matching. Numerical examples and experimental studies were conducted to verify and validate the concepts and model/algorithms developed. The results showed the effectiveness and applicability of proposed method.     

A hypernetwork-based approach to collaborative retrieval and reasoning of engineering design knowledge

Complex product development increasingly entails creation and sharing of design knowledge in a collaborative and integrated working environment. In this context, it has become a central issue to address the multifaceted feature of design knowledge for such a collaborative knowledge sharing scheme. This paper proposes a hypernetwork-based approach to explicitly capturing the relationships between various elements in a multifaceted knowledge representation. Specifically, a knowledge hypernetwork model is constructed, which is composed of a designer network, a product network, an issue network and a knowledge unit network. The relationships between various nodes from different networks are identified and defined according to specific node properties. In addition, topological characteristics of the hypernetwork structure are analyzed together with the statistical indicators. Based on this model, the Bayesian approach is adopted to conduct the collaborative reasoning process whereby knowledge elements relevant to the current design task are recommended according to the issues to be resolved and the current design context. A case study conducted in this work shows that the proposed approach is effective in capturing the complex relationships between multi-faceted knowledge elements and enables collaborative retrieval and reasoning of knowledge records.