Jixin Ma

A framework for historical case-based reasoning

This paper presents a framework for Historical Case-Based Reasoning (HCBR) which allows the expression of both relative and absolute temporal knowledge, representing case histories in the real world. The formalism is founded on a general temporal theory that accommodates both points and intervals as primitive time elements. A case history is formally defined as a collection of (time-independent) elemental cases, together with its corresponding temporal reference. Case history matching is two-fold, i.e., there are two similarity values need to be computed: the non-temporal similarity degree and the temporal similarity degree. On the one hand, based on elemental case matching, the non-temporal similarity degree between case histories is defined by means of computing the unions and intersections of the involved elemental cases. On the other hand, by means of the graphical presentation of temporal references, the temporal similarity degree in case history matching is transformed into conventional graph similarity measurement.

Reified Temporal Logics: An Overview

There are three main approaches to the representationof temporal information in AI literature: theso-called method of temporal arguments thatsimply extends functions and predicates of first-orderlanguage to include time as the additional argument;modal temporal logics which are extensions ofthe propositional or predicate calculus with modaltemporal operators; and reified temporal logicswhich reify standard propositions of some initiallanguage (e.g., the classical first-order or modallogic) as objects denoting propositional terms. Theobjective of this paper is to provide an overview onthe temporal reified approach by looking closely atsome representative existing systems featuring reifiedpropositions, including those of Allen, McDermott,Shoham, Reichgelt, Galton, and Ma and Knight. We shalldemonstrate that, although reified logics might bemore complicated in expressing assertions about somegiven objects with respect to different times, theyaccord a special status to time and therefore haveseveral distinct advantages in talking about someimportant issues which would be difficult (if notimpossible) to express in other approaches.

Time representation: A taxonomy of temporal models

The objective of the paper is to provide a taxonomy of temporal systems according to three fundamental considerations: the assumed axiomatic theory, the expressiveness, and the mechanisms for inference which are provided. There is an discussion of the significance of the key features of the taxonomy for computer modelling of temporal events. A review considers the most significant representative systems with respect to these issues, including those due to Bruce, Allen and Hayes, Vilain, McDermott, Dechteret al., Kahn and Gorry, Kowalski and Sergot, Bacchuset al., and Knight and Ma. A tabular comparison of systems is given according to their main structural features. In conclusion, the characteristics of a general axiomatic system capable of representing all the features of these models is discussed.

A case based reasoning approach for the monitoring of business workflows

This paper presents an approach for the intelligent diagnosis and monitoring of business workflows based on operation data in the form of temporal log data. The representation of workflow related case knowledge in this research using graphs is explained. The workflow process is orchestrated by a software system using BPEL technologies within a service-oriented architecture. Workflow cases are represented in terms of events and their corresponding temporal relationships. The matching and CBR retrieval mechanisms used in this research are explained and the architecture of an integrated intelligent monitoring system is shown. The paper contains an evaluation of the approach based on experiments on real data from a university quality assurance exam moderation system. The experiments and the evaluation of the approach is presented and is shown that a graph matching based similarity measure is capable to diagnose problems within business workflows. Finally, further work on the system and the extension to a full intelligent monitoring and process optimisation system is presented.

Representing the dividing instant

The so-called dividing instant (DI) problem is an ancient historical puzzle encountered when attempting to represent what happens at the boundary instant which divides two successive states. The specification of such a problem requires a thorough exploration of the primitives of the temporal ontology and the corresponding time structure, as well as the conditions that the resulting temporal models must satisfy. The problem is closely related to the question of how to characterize the relationship between time periods with positive duration and time instants with no duration. It involves the characterization of the ‘closed’ and ‘open’ nature of time intervals, i.e. whether time intervals include their ending points or not. In the domain of artificial intelligence, the DI problem may be treated as an issue of how to represent different assumptions (or hypotheses) about the DI in a consistent way. In this paper, we shall examine various temporal models including those based solely on points, those based solely on intervals and those based on both points and intervals, and point out the corresponding DI problem with regard to each of these temporal models. We shall propose a classification of assumptions about the DI and provide a solution to the corresponding problem.

A Temporal Database Model Supporting Relative and Absolute Time

This paper presents a temporal database model which allows the expression of relative temporal knowledge of data transaction and data validity times. The system is founded on an extension to Allens axiomitization of time, given previously by the authors, which takes both intervals and points as primitive time elements. A general retrieval mechanism is presented for a database with a purely relative temporal knowledge which allows queries with temporal constraints in terms of any logical combination of Allens temporal predicates. When absolute temporal duration knowledge is added, the consistency checking algorithm upon which the inference mechanism is based reduces to a linear programming problem. A class of databases, termed time-limited databases, is introduced as a practical solution to the problem of computational complexity of retrieval

A Reified Temporal Logic

This paper presents a reified temporal logic for representing and reasoning about temporal and non-temporal relationships between non-temporal assertions. A clear syntax and semantics for the logic is formally provided. Three types of predicates, temporal predicates, non-temporal predicates and meta-predicates, are introduced. Terms of the proposed language are partitioned into three types, temporal terms, non-temporal terms and propositional terms. Reified propositions consist of formulae with each predicate being either a temporal predicate or a meta-predicate. Meta-predicates may take both temporal terms and propositional terms together as arguments or take propositional terms alone. A standard formula of the classical first-order language with each predicate being a non-temporal predicate taking only non-temporal terms as arguments is reified as just a propositional term. A general time ontology has been provided which can be specialized to a variety of existing temporal systems. The new logic allows one to predicate and quantify over propositional terms while according a special status of time; for example, assertions such as ‘effects cannot precede their causes’ is ensured in the logic, and some problematic temporal aspects including the delay time between events and their effects can be conveniently expressed. Applications of the logic are presented including the characterization of the negation of properties and their contextual sentences, and the expression of temporal relations between actions and effects.

Primitive Intervals versus Point-Based Intervals: Rivals or Allies?

The notion of time is a very interesting and exciting subject both in science and everyday life. One of the fundamental questions is: what is time composed of? While the traditional time structure is based on a set of points, a notion that has been prevalently adopted in classical physics and mathematics, it has also been noticed that intervals have been widely adopted for expression of commonsense temporal knowledge, especially in the domain of artificial intelligence. However, there has been a long-standing debate on whether intervals should be treated as primitive or not, leading to two different approaches to the treatment of intervals. In the first, intervals are modelled as derived objects constructed from points, e.g. sets of points, or pairs of points. In the second, intervals are taken as primitive themselves. This article provides a critical examination of these two approaches. By means of proposing a definition of intervals in terms of points and types, we shall demonstrate that, while the two different approaches have been viewed as rivals in the literature, they are actually reducible to logically equivalent expressions under some requisite interpretations, and therefore they can also be viewed as allies.

Representing temporal knowledge in legal discourse

This paper presents a formalism for representing temporal knowledge in legal discourse that allows an explicit expression of time and event occurrences. The fundamental time structure is characterized as a well‐ordered discrete set of primitive times, i.e. non‐decomposable intervals with positive duration or points with zero duration), from which decomposable intervals can be constructed. The formalism supports a full representation of both absolute and relative temporal knowledge, and a formal mechanism for checking the temporal consistency of a given set of legal statements is provided. The general consistency checking algorithm which addresses both absolute and relative temporal knowledge turns out to be a linear programming problem, while in the special case where only relative temporal relations are involved, it becomes a simple question of searching for cycles in the graphical representation of the corresponding legal text.

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010

th The purpose of the 11 Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2010) held on June 9 11, 2010 in London, United Kingdom was to bring together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. Our conference officers selected the best 15 papers from those papers accepted for presentation at the conference in order to publish them in this volume. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rounds of rigorous review. In Chapter 1, Cai Luyuan et al. Present a new method of shape decomposition based on a refined morphological shape decomposition process. In Chapter 2, Kazunori Iwata et al. propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks (ANNs). In Chapter 3, Viliam imko et al. describe a model-driven tool that allows system code to be generated from use-cases in plain English. In Chapter 4, Abir Smiti and Zied Elouedi propose a Case Base Maintenance (CBM) method that uses machine learning techniques to preserve the maximum competence of a system. In Chapter 5, Shagufta Henna and Thomas Erlebach provide a simulation based analysis of some widely used broadcasting schemes within mobile ad hoc networks (MANETs) and propose adaptive extensions to an existing broadcasting algorithm.