Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external events and simultaneous action...

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events, and contexts. A formal specification of a domains temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge.

: In certain areas of artificial intelligence there is need to represent continuous change and to make statements that are interpreted with respect to time intervals rather than time points. To this end we develop a modal temporal logic based on time intervals, a logic which can be viewed as a generalization of pointbased modal temporal logic. We discuss related logics, give an intuitive presentation of the new logic, and define its formal syntax and semantics. We make no assumption about the underlying nature of time, allowing it to be discrete (such as the natural numbers) or continuous (such as the rationals or the reals), linear or branching, complete (such as the reals) or not (such as the rationals). We show, however, that there are formulas in the logic that allow us to distinguish all these situations. We also give a translation of our logic into first-order logic, which allows us to apply some results on first-order logic to our modal one. Finally, we consider the difficulty o...

We have defined a knowledge-based framework for solving the task of creating abstract, intervalbased concepts from time-stamped clinical datathe knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes the temporal-abstraction task into five tasks; a formal mechanism is proposed for solving each subtask. The KBTA framework emphasizes the explicit representation of the knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse, and sharing. The RSUM system implements the KBTA method. We tested RSUM in several clinical domains in which the task of monitoring patients is prominent. In particular, we tested the KBTA framework in the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert a diabetes-therapy temporal-abstraction knowledge base. Two diabetes-therapy experts (including the first one) created temporal abstractions relevant to the therapy-monitoring task from about 800 points of data from cases of diabetic patients. The RSUM system generated about 80% of the abstractions agreed by both experts; about 97% of the overall generated abstractions were valid. We discuss the advantages and limitations of the current architecture.

This paper presents an implemented system for recognizing the occurrence of events described by simple spatial-motion verbs in short image sequences. The semantics of these verbs is specified with event-logic expressions that describe changes in the state of force-dynamic relations between the participants of the event. An efficient finite representation is introduced for the infinite sets of intervals that occur when describing liquid and semi-liquid events. Additionally, an efficient procedure using this representation is presented for inferring occurrences of compound events, described with event-logic expressions, from occurrences of primitive events. Using force dynamics and event logic to specify the lexical semantics of events allows the system to be more robust than prior systems based on motion profile. 1.

Provision of automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system, and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task, and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a timeoriented patient database; and (4) allow acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed and evaluated independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who have AIDS. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer.

Terminological systems, such as KL-ONE and K-Rep, are widely used in AI to represent and reason with concept descriptions. They compute subsumption relations between concepts and automatically classify concepts into a taxonomy. Each concept in the taxonomy describes a set of possible instances which are a superset of those described by its descendants. One limitation of current systems is their inability to handle complex compositions of concepts, such as constraint networks where each node is described by an associated concept. For example, plans are often represented (in part) as collections of actions related by a rich variety of temporal constraints. The T-REX system integrates terminological reasoning with constraint network reasoning to classify such plans, producing a "terminological" plan library. T-REX also introduces a new view of plan recognition as a process which dynamically partitions the plan library by modalities, e.g., necessary, possible and impo...

This paper describes an implemented computer program that recognizes the occurrence of simple spatial motion events in simulated video input. The program receives an animated line-drawing as input and produces as output a semantic representation of the events occurring in that animation. This paper suggests that the notions of support, contact, and attachment are crucial to specifying many simple spatial motion event types and presents a logical notation for describing classes of events that incorporates such notions as primitives. It then suggests that the truth values of such primitives can be recovered from perceptual input by a process of counterfactual simulation, predicting the effect of hypothetical changes to the world on the immediate future. Finally, it suggests that such counterfactual simulation is performed using knowledge of naive physical constraints such as substantiality, continuity, gravity, and ground plane. This paper describes the algorithms that incorporate these ideas in the program and illustrates the operation of the program on sample input.

This paper describes a reasoning system based on a temporal logic that can solve planning problems along the lines of traditional planning systems. Because it is cast as inference in a general representation, however, the ranges of problems that can be described is considerably greater than in traditional planning systems. In addition, other modes of plan reasoning, such as plan recognition or plan monitoring, can be formalized within the same framework. 1 INTRODUCTION There is strong interest currently in designing planning systems that can reason about realistic worlds. In moving from the toy-world domains that characterized early work, researchers are looking at a wide range of issues, including reasoning in uncertain worlds, interacting with processes and events beyond the agent's direct control, and controlling mechanisms in real-time (i.e. robotics). One of the problems faced in extending existing frameworks is the weak expressiveness of the representation of the acti...