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.     

On-demand e-supply chain integration: A multi-agent constraint-based approach

With e-business emerging as a key enabler to drive supply chains, the focus of supply chain management has been shifted from production efficiency to customer-driven and partnership synchronization approaches. This strategic shift depends on the match between the demands and offerings that deliver the services. To achieve this, we need to coordinate the flow of information among the services, and link their business processes under various constraints. Existing approaches to this problem have relied on complete information of services and resources, and have failed to adequately address the dynamics and uncertainties of the operating environments. The real-world situation is complicated as a result of undetermined requirements of services involved in the chain, unpredictable solutions contributed by service providers, and dynamic selection and aggregation of solutions to services. This paper examines an agent-mediated approach to on-demand e-business supply chain integration. Each agent works as a service broker, exploring individual service decisions as well as interacting with each other for achieving compatibility and coherence among the decisions of all services. Based on the framework, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

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.     

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 constraint-based topic modeling approach for name disambiguation

Name ambiguity refers to a problem that different people might be referenced with an identical name. This problem has become critical in many applications, particularly in online bibliography systems, such as DBLP and CiterSeer. Although much work has been conducted to address this problem, there still exist many challenges. In this paper, a general framework of constraint-based topic modeling is proposed, which can make use of user-defined constraints to enhance the performance of name disambiguation. A Gibbs sampling algorithm that integrates the constraints has been proposed to do the inference of the topic model. Experimental results on a real-world dataset show that significant improvements can be obtained by taking the proposed approach.     

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 fuzzy constraint-based approach to data reconciliation in material flow analysis

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least-squares minimization. Here, we use a fuzzy set-based approach, replacing Gaussian likelihood functions by fuzzy intervals, and a leximin criterion. We show that the setting of fuzzy sets provides a generalized approach to the choice of estimated values, that is more flexible and less dependent on oftentimes debatable probabilistic justifications. It potentially encompasses interval-based formulations and the least squares method, by choosing appropriate membership functions and aggregation operations. This paper also lays bare the fact that data reconciliation under the fuzzy set approach is viewed as an information fusion problem, as opposed to the statistical tradition which solves an estimation problem.     

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 graphical approach to relational reasoning

Relational reasoning is concerned with relations over an unspecified domain of discourse. Two limitations to which it is customarily subject are: only dyadic relations are taken into account; all formulas are equations, having the same expressive power as first-order sentences in three variables. The relational formalism inherits from the Peirce-Schröder tradition, through contributions of Tarski and many others.Algebraic manipulation of relational expressions (equations in particular) is much less natural than developing inferences in first-order logic; it may in fact appear to be overly machine-oriented for direct hand-based exploitation.The situation radically changes when one resorts to a convenient representation of relations based on labeled graphs. The paper provides details of this representation, which abstracts w.r.t. inessential features of expressions.Formal techniques illustrating three uses of the graph representation of relations are discussed: one technique deals with translating first-order specifications into the calculus of relations; another one, with inferring equalities within this calculus with the aid of convenient diagram-rewriting rules; a third one with checking, in the specialized framework of set theory, the definability of particular set operations. Examples of use of these techniques are produced; moreover, a promising approach to mechanization of graphical relational reasoning is outlined.     

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 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 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.     

Temporal Constraints: A Survey

Temporal Constraint Satisfaction is an information technology useful for representing and answering queries about temporal occurrences and temporal relations between them. Information is represented as a Constraint Satisfaction Problem (CSP) where variables denote event times and constraints represent the possible temporal relations between them. The main tasks are two: (i) deciding consistency, and (ii) answering queries about scenarios that satisfy all constraints. This paper overviews results on several classes of Temporal CSPs: qualitative interval, qualitative point, metric point, and some of their combinations. Research has progressed along three lines: (i) identifying tractable subclasses, (ii) developing exact search algorithms, and (iii) developing polynomial-time approximation algorithms. Most available techniques are based on two principles: (i) enforcing local consistency (e.g. path-consistency) and (ii) enhancing naive backtracking search.     

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 fully geometric approach for interactive constraint-based structural equilibrium design

This paper introduces computational techniques to support architects and structural designers in the shaping of strut-and-tie networks in static equilibrium. Taking full advantage of geometry, these techniques build on the reciprocal diagrams of graphic statics and enhance the interactive handling of them with two devices: (1) nodes–considered as the only variables–are constrained within Boolean combinations of graphic regions, and (2) the user modifies the diagrams by means of successive operations whose geometric properties do not at any time jeopardize the static equilibrium. This constructive approach enables useful design-oriented capabilities: a graphical control of multiple solutions, the direct switching of the dependencies hierarchy, the execution of dynamic conditional statements using static constraints, the computation of interdependencies, and coordinate-free methods for ensuring consistency between certain continuums of solutions. The paper describes a computer implementation of these capabilities.     

A constraint-based dynamic geometry system

Dynamic geometry systems are tools for geometric visualization. They allow the user to define geometric elements, establish relationships between them and explore the dynamic behavior of the remaining geometric elements when one of them is moved. The main problem in dynamic geometry systems is the ambiguity that arises from operations that lead to more than one possible solution. Most dynamic geometry systems deal with this problem in such a way that the solution selection method leads to a fixed dynamic behavior of the system. This is specially annoying when the behavior observed is not the one the user intended.In this work we propose a modular architecture for dynamic geometry systems built upon a set of functional units which will allow us to apply some well-known results from the Geometric Constraint Solving field. A functional unit called filter will provide the user with tools to unambiguously capture the expected dynamic behavior of a given geometric problem.     

Process algebra approach to reasoning about concurrent actions

A reasonable transition rule is proposed for synchronized actions and some equational properties ofbisimilarity and weak bisimilarity in the process algebra for reasoning about concurrent actions are presented.