Representing and reasoning about incomplete and indefinite qualitative temporal information is an essential part of many artificial intelligence tasks. An interval-based framework and a point-based framework have been proposed for representing such temporal information. In this paper, we address two fundamental reasoning tasks that arise in applications of these frameworks: Given possibly indefinite and incomplete knowledge of the relationships between some intervals or points, (i) find a scenario that is consistent with the information provided, and (ii) find the feasible relations between all pairs of intervals or points. For the point-based framework and a restricted version of the intervalbased framework, we give computationally efficient procedures for finding a consistent scenario and for finding the feasible relations. Our algorithms are marked improvements over the previously known algorithms. In particular, we develop an O(n 2 ) time algorithm for finding one co...

This paper presents this algorithm, gives demonstrations of an implemented system, and describes the results of some preliminary empirical studies, which lend support to the algorithm

This paper consists of two parts. The first part discusses commonsense knowledge about events as manifested in language. Three kinds of knowledge are identified: compositional, durational, and aspectual. Compositional knowledge concerns internal structuring of events into preparatory, initial, main (the body), final, and resulting stages. Durational knowledge concerns durational relations between events and stages of the same event. Durational knowledge can be expressed as qualitative dependencies among the parameters of the event and as its time scale. The notion of time scale is introduced and related to shared cyclical events (time units). In discussing

Much temporal information is qualitative information such as ‘‘The Cuban Missile crisis took place during Kennedy’s presidency,’ ’ where only the ordering of the end points of the two events is specified. A point and an interval algebra have been proposed for representing qualitative temporal information about the relationships between pairs of intervals and pairs of points, respectively. In this thesis, we address two fundamental reasoning tasks that arise in these algebras: Given (possibly indefinite) knowledge of the relationships between some intervals or points, • find a scenario that is consistent with the information provided, and • find the feasible relationships between some or all pairs of intervals or points. Solutions to these tasks have applications in natural language processing, planning, plan recognition, diagnosis, and knowledge-based systems. For the task of finding consistent scenarios the main results are as follows. For the point algebra, we develop an O(n 2) time algorithm that is an O(n) improvement over the previously known algorithm, where n is the number of points. For the interval algebra,

Time is an important aspect of information in medical domains. In this paper, we adopt Allen's influential interval algebra framework for representing temporal information. The interval algebra allows the representation of indefinite and incomplete information which is necessary in many applications. However, answering interesting queries in this framework has been shown to be almost assuredly intractable. We show that when the representation language is sufficiently restricted we can develop efficient algorithms for answering interesting classes of queries including: (i) determining whether a formula involving temporal relations between events is possibly true and necessarily true; and (ii) answering aggregation questions where the set of all events that satisfy a formula are retrieved. We also show, by examining applications of the interval algebra discussed in the literature, that our restriction on the representation language often is not overly restrictive in practice. 1 Introduct...

We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Casta~neda, namely, that the simple rule `(A knows that P ) implies P ' apparently does not hold if P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P , including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (for example, written narrative) in order to fully capture certain features of their semantics. (This document is SUNY Buffalo Department of Computer Science Technical Report No. 95-49B, as well as SUNY Buffalo Center for Cognitive Science Tec...

This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the cognitive psychologists and language researchers in our group, and a computational model of a reader of narrative text is being developed by the AI researchers, based in part on these theories and results and in part on research on knowledge representation and reasoning. This proposal describes the knowledge-representation and natural-language-processing issues involved in the computational implementation of the theory; discusses a contrast between communicative and narrative uses of language and of the relation of the narrative text to the story world it describes; investigates linguistic, literary, and hermeneutic dimensions of our research; presents a computational investigation of subjective sentences and reference in narrative; studies children’s acquisition of the ability to take third-person perspective in their own storytelling; describes the psychological validation of various linguistic devices; and examines how readers develop an understanding of the geographical space of a story. This report is a longer

This paper discusses the theoretical background and some of the results of an interdisciplinary, cognitive-science research project on the comprehension of narrative text. The unifying theme of our work has been the notion of a deictic center: a mental model of spatial, temporal, and character information contributed by the reader of the narrative and used by the reader in understanding the narrative. We examine the deictic center in the light of our investigations from the viewpoints of linguistics, cognitive psychology, individual differences (language pathology), literary theory of narrative, and artificial intelligence.

rapaport%buffaloecsnet-relay We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Castaneda, namely, that the simple rule • (A knows that p) implies P, apparently does not hold if P contains a quasi-indicator. We present a single rule, in the context of an AI representation and reasoning system, that holds for all propositions P, including quasi-indexical ones. In so doing, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended text (e.g., discourse or narrative) in order to fully capture certain features of their semantics. 1. INTRODUCTION. How are knowledge and belief related? The standard philosophical analysis, dating back at least to Plato (Theaetetus 201), is that knowledge is justified true belief (but cf. Gettier 1963). In this paper, we describe some issues that are literally in the field of knowledge representation-issues in the representation of knowledge reports, where knowledge is treated as true belief. I In particular, we

Contents 1 Introduction ............................................................................................. 1 1.1 A Simple Example ............................................................................... 1 1.2 AI Perspective ...................................................................................... 3 1.3 ESU Perspective .................................................................................. 5 1.4 Goals for Research and Development ................................................. 7 1.5 Thesis Overview .................................................................................. 8 2 Background .............................................................................................. 9 2.1 Introduction ......................................................................................... 9 2.2 Influential Work in Temporal Reasoning ........................................... 9 2.2.1 James Allen ........