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 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 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     

Integrating specifications: A similarity reasoning approach

Requirements analysis usually results in a set of different specifications for the same system, which must be integrated. Integration involves the detection and elimination of discrepancies between them. Discrepancies may be due to differences in representation models, modeling perspectives or practices. As instances of the semantic heterogeneity problem (Gangopadhyay and Barsalou, 1991), discrepancies are broader than logical inconsistencies, and therefore not always detectable using theorem proving. This paper proposes an approach to their detection using meta-modeling and similarity analysis. Specification components are classified under a meta-model of domain independent semantic modeling abstractions and thereby compared according to a newly developed model of similarity. Similarity analysis results in an isomorphic mapping between them, which can be used as a basis for reconciling and merging them. The approach is extensible in the sense that it accommodates different models for representing specifications, and analysis scales up to manage large, complex specification because the complexity of similarity analysis is polynomial.     

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.     

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.     

On point-duration networks for temporal reasoning

We present here a point-duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time. Thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations, subsuming in this way several point-based approaches to temporal reasoning. We deal with some reasoning tasks within the new models and we show that the main problem, deciding consistency, is NP-complete. However, tractable special cases are identified and we show efficient algorithms for checking consistency, finding a solution and obtaining the minimal network.