Closed Terminologies and Temporal Reasoning in Description Logic for Concept and Plan Recognition

Description logics are knowledge representation formalisms in the tradition of frames and semantic networks, but with an emphasis on formal semantics. A terminology contains descriptions of concepts, such as UNIVERSITY, which are automatically classified ina taxonomy via subsumption inferences. Individuals such as COLUMBIA are described in terms of those concepts. This thesis enhances the scope and utility of description logics by exploiting new completeness assumptions during problem solving and by extending the expressiveness of descriptions. First, we introduce a predictive concept recognition methodology based on a new closed terminology assumption (CTA). The terminology is dynamically partitioned by modalities (necessary, optional, and impossible) with respect to individuals as they are specified. In our interactive configuration application, a user incrementally specifies an individual computer system and its components in collaboration with a configuration engine. Choices can be made in any order and at any level of abstraction. We distinguish between abstract and concrete concepts to formally define when an individual's description may be considered finished. We also exploit CTA, together with the terminology's subsumption-based organization, to efficiently track the types of systems and components consistent with current choices, infer additional constraints on current choices, and appropriately restrict future choices. Thus, we can help focus the efforts of both user and configuration engine. This work is implemented in the K-REP system. Second, we show that a new class of complex descriptions can be formed via constraint networks over standard descriptions. For example, we model plans as constraint networks whose nodes represent actions.Arcs represent qualitative and metric temporal constraints, plusco-reference constraints, between actions. By combining terminological reasoning with constraint satisfaction techniques, subsumption is extended to constraint networks, allowing automatic classification of a plan library. This work is implemented in the T-REX system, which integrates and builds upon an existing description logic system (K-REP or CLASSIC) and temporal reasoner (MATS). Finally, we combine the preceding, orthogonal results to conduct predictive recognition of constraint network concepts. As an example,this synthesis enables a new approach to deductive plan recognition,illustrated with travel plans. This work is also realized in T-REX

Highlighting User Related Advice

Research on explanation techniques for expert systems has demonstrated that (1) explanations are most effective when they address the user's needs and (2) it is necessary to augment explanations with information that is missing from the expert system‘s reasoning. It is our thesis that explanation content can also be improved by removing extraneous information from the system's reasoning and recognizing the remainder to emphasize user concerns. To test our ideas, we have developed an interactive natural language problem-solving system called ADVISOR which advises students on course selection. Previously, we have reported on our methodology for deriving user goals from the discourse, representing different points of view in the knowledge base and inferring user-oriented advice with a rule-based system that employs information from the appropriate perspective to address user goals. In this paper, we describe a model for pruning an explanation to highlight the role of the user's goal. The model is part of ADVISOR's natural language generation component. We demonstrate its efficacy with examples of different advice that ADVISOR provides for the same query in the context of different goals

Inferring User-Oriented Advice in ADVISOR

To be considered cooperative, an expert system must be easy to interact with. It must produce responses that are contextually appropriate, sensitive to the needs of users and suited to the user's level of sophistication. Expert system responses must be explained in a clear and concise fashion, often to untrained users. We have constructed a student advising system called ADVISOR, which contains a rule-based expert system, to test our ideas in natural language understanding, user modeling, use-oriented explanation, and text generation. ADVISOR assists computer science majors in course selection by providing information and offering advice during a natural language question-answering dialogue. In previous papers, we presented an overview of ADVISOR and detailed out methodologies for deriving user goals from the discourse and expressing expert system reasoning in natural language that emphasizes use goals (McKeown et al., 1985; McKeown, 1988; McKeown and Weida, 1988). In this paper we focus on how the expert with supporting justifications and additional relevant observations. Since advice is geared to the individual student, ADVISOR can provide different answers to the same questions as well as different explanations for the same answer. Indeed, its advice may change, along with the student‘s goals, as conversation progresses

Terminological Constraint Network Reasoning and its Application to Plan Recognition

Terminological systems in the tradition of KL-ONE 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 having well-founded semantics. 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 and other 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 theory of plan recognition as a deductive process which dynamically partitions the plan library by modalities, e.g., necessary, possible and impossible, while observations are made. Plan recognition is guided by the plan library's terminological nature. Varying assumptions about the accuracy and monotonicity of the observations are addressed. Although this work focuses on temporal constraint networks used to represent plans, terminological systems can be extended to encompass constraint networks in other domains as well

Knowledge Representation and Reasoning with Definitional Taxonomies

We provide a detailed overview of knowledge representation issues in general and terminological knowledge representation in particular. Terminological knowledge representation, which originated with KL-ONE, is an object-centered approach in the tradition of semantic networks and frames. Terminological systems share three distinguishing characteristics: (1) They are intended to support the definition of conceptual terms comprising a "terminology" and to facilitate reasoning about such terms. As such, they are explicitly distinguished from assertional systems which make statements of fact based on some terminology. (2) Their concepts are arranged in a taxonomy so that the attributes of a concept apply to its descendants without exception. Thus, the proper location of any concept within the taxonomy can be uniquely determined from the concept‘s definition by an automatic process known as classification. (3) They restrict the expressiveness of their language to achieve relatively efficient performance. We first survey important general issues in the field of knowledge representation, consider the semantics of concepts and their interrelationship, and examine the intertwined notions of taxonomy and inheritance. After discussing classification, we present a number of implemented terminological systems in detail, along with several hybrid systems which couple terminological and assertional reasoning components. We conclude by assessing the current state of the art in terminological knowledge representation

Reporting guidelines for human microbiome research: the STORMS checklist

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results. The STORMS tool provides guidance for concise and complete reporting of microbiome studies to facilitate manuscript preparation, peer review, reader comprehension of publications, and comparative analysis of published results