摘要:

Current approaches to qualitative reasoning are largely based on a fixed framework for modeling the physical world and concentrate on the reasoning methods that support qualitative reasoning. This paper argues that we need several levels of abstraction and different viewpoints on how to model the physical world, in order to create systems that reason about the physical world in a flexible way. We present a framework that integrates the three basic approaches to qualitative reasoning and show how this framework can be used as a basis for a flexible qualitative reasoning system.

ISSN:0883-9514    

DOI:10.1080/08839518908949919

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

HIQUAL is a component-oriented deep modeling language that supports the modeling of component hierarchies and the representation and analysis of temporal relations. We describe the semantics of a system of components as a set of temporally and causally related temporal intervals that are denoted by dynamic states and events of the components. Thus, we obtain a uniform semantics for single components, for a system of horizontally connected components at the same level, and for a system of vertically connected components at different levels of abstraction. We claim that in our approach parallelism and other temporal aspects including temporal uncertainty are more naturally represented than in other approaches, in particular those using global state semantics.

ISSN:0883-9514    

DOI:10.1080/08839518908949920

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Intelligent process control may be viewed as encompassing four major tasks. An intelligent agent must monitor the target system to obtain the values of relevant stale variables in order to detect problems and to ascertain the status of the components that may be employed in responding to those problems. An intelligent agent must determine plans for managing the current situation. An intelligent agent must select a response (the “best” one) through a process of plan evaluation. Finally, to carry out the chosen response, the agent must perform plan execution. While monitoring and execution are relatively straightforward operations, plan determination and plan evaluation may be accomplished in a number of ways that vary in their relative depth of reasoning. In this paper we sketch an analysis for the reasoning underlying plan determination and evaluation tasks for a class of intelligent control systems that attempt to “provide a safety function.” This analysis has two objectives: to illustrate a domain-independent mode of analysis for examining progressively deeper models, and to make the analysis available to those interested in building systems that provide safety functions.

ISSN:0883-9514    

DOI:10.1080/08839518908949921

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

I describe an approach to the problem of forming hypotheses about hidden mechanisms within devices—the “black box” problem for physical systems. The approach involves enumerating different causal structures for devices, placing an ordering on these hypothesis types, and carefully controlling the construction of hypotheses, as well as enumerating a set of physical and causal constraints to prune hypotheses. I relate in detail the performance of an implemented causal modeling system on the surprisingly puzzling pocket tire gauge. Results from several examples indicate that the ideas presented support capabilities for maintaining manageably sized hypothesis sets and for making fine distinctions among hypotheses.

ISSN:0883-9514    

DOI:10.1080/08839518908949922

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

AI research in qualitative physics and causal models suggests new approaches to teaching people about science and engineering. We have been investigating the form that such models need to take to be effective within intelligent learning environments. The subject matter we have focused on is understanding how electrical circuits work, but the approach can be generalized to other subjects. Two key hypotheses have emerged from our research. The first is that in order to understand a physical system, students need to acquire causal mental models for how the system works. Further, it is not enough to have just a single mental model; students need alternative mental models that represent the systems behavior from different but related perspectives, such as at the macroscopic and microscopic levels. The second hypothesis is that in order to make causal understanding feasible in the initial stages of learning, students have to be introduced to simplified models. These models are then gradually refined into more sophisticated mental models. The questions addressed in this article are: What are the properties of an easily learnable, coherent set of initial models? What are the types of evolutions needed for students to acquire a more powerful set of models with broad utility?

ISSN:0883-9514    

DOI:10.1080/08839518908949923

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Future expert systems for understanding physical mechanisms will probably employ causal models as the foundation of their expertise, and the problem of acquiring these causal models is important. This paper explores one possibility, that of acquiring causal models by understanding natural language explanations of these mechanisms. It identifies six different research issues in which understanding an explanation requires knowledge-based reasoning, and proposes approaches to these problems within an integrated natural language-based causal model acquisition system.

ISSN:0883-9514    

DOI:10.1080/08839518908949924

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This paper introduces a domain model for use in diagnostic problem solving. It emphasizes functional decomposition, rather than behavior, cause-effect, or spatial structure, and represents explicitly the faults of a system together with its possible explanations. Various possible problem solving actions that operate over a function-fault model are introduced.

ISSN:0883-9514    

DOI:10.1080/08839518908949925

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

This paper describes an attempt to augment heuristic reasoning with deeper reasoning. The context of the work is the DIVA Expert System that has been designed to diagnose faults on rotating machinery. DIVA is essentially based on heuristic knowledge. We will describe how it should be possible to enhance its reasoning capabilities by enabling DIVA to reason on the causality of involved phenomena.

ISSN:0883-9514    

DOI:10.1080/08839518908949926

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Experienced diagnosticians draw on a rich variety of reasoning techniques, ranging from the association of symptoms and diseases to causal reasoning about disease mechanisms and first-principle analysis grounded in basic science. The entire range of diagnostic reasoning strategies is also necessary for a computer program to be truly proficient and robust. The development of such a program has been impeded by the inherent complexity of the domain and the consequent lack of an adequate methodology for knowledge organization and integration. We present a methodology for structuring medical knowledge and managing its complexity. We illustrate this methodology in the context of an experimental knowledge base in the domain of jaundice. We believe that this systematic knowledge base design will support the development of automated reasoning methods that span the entire range of reasoning techniques used by physicians.

ISSN:0883-9514    

DOI:10.1080/08839518908949927

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor

摘要:

Problem solving based on compiled associations between elements of the decision space and data is an efficient mode of reasoning for a large percentage of situations faced by an expert. But in some (usually small) percentage of cases, compiled associations are not enough by themselves to lead to correct results. Reasoning from “deeper” levels of understanding offers the advantage of producing correct results even in atypical cases, but at the cost of expanding more computational resources. Thus the trade-off between compiled level systems and deep level systems is between computational efficiency (at the compiled level) and problem-solving generality (at the deep level). We describe a hybrid system containing elements of both deep level reasoning and compiled level reasoning. More particularly, we propose a problem-solving architecture for category-based diagnostic problem solving which at the compiled level centers on classification problem solving and at the deep level uses a type of function-based reasoning. We concentrate in this report on the interaction between the compiled and deep level units and on the mechanisms of function-based reasoning that we employ. We show how our function-based consequence-finding problem solver can be focused by problem solving at the compiled level and how, through such interaction, we obtain the computational efficiency characteristic of compiled level problem solving while retaining the robustness characteristic of deep level problem solving.

ISSN:0883-9514    

DOI:10.1080/08839518908949928

出版商:Taylor & Francis Group    

年代:1989

数据来源: Taylor