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.

Case-based reasoning is a recent approach to problem solving and learning that has got a lot of attention over the last few years. Originating in the US, the basic idea and underlying theories have spread to other continents, and we are now within a period of highly active research in case-based reasoning in Europe, as well. This paper gives an overview of the foundational issues related to case- based reasoning, describes some of the leading methodo- logical approaches within the field, and exemplifies the current state through pointers to some systems. Initially, a general framework is defined, to which the subsequent descriptions and discussions will refer. The framework is influenced by recent methodologies for knowledge level descriptions of intelligent systems. The methods for case retrieval, reuse, solution testing, and learning are summa-rized, and their actual realization is discussed in the light of a few example systems that represent different CBR approaches. We also discuss the role of case-based methods as one type of reasoning and learning method within an integrated system architecture.

One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparativ...

. Problem solving in weak theory domains should compensate for the lack of strong theories by combining the various other knowledge types involved. Such methods should be able to effectively combine general domain knowledge with specific case knowledge. A method is described that utilises a presumably extensive and dense model of general domain knowledge as explanatory support for case-based problem solving and learning. A generic reasoning method - captured in what is called the ACTIVATE-EXPLAIN-FOCUS cycle - is able to utilise a rich knowledge model in producing contextdependent explanations. A specialisation of this method for each of the main subprocesses of case-based reasoning is presented, and illustrated with examples. 1 Introduction A growing part of the AI community is concerned with approaches that integrate several types of knowledge and reasoning methods (see for example [David et. al., 1993]). Although case-based reasoning is a rather new addition to the curre...

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.

Problem solving methods (PSMs) are domain-independent reasoning components, which specify patterns of behavior which can be reused across applications. While the availability of extensive PSM libraries and the emerging consensus on PSM specification languages indicate the maturity of the field, a number of important research issues are still open. In particular, very little progress has been achieved on foundational and methodological issues. Existing libraries of PSMs lack a clear theoretical basis and only provide weak support for the method development process, usually in the form of informal guidelines. In this paper we will address these issues by illustrating a framework which characterizes PSMs in terms of problem commitments, problem-solving paradigms and domain assumptions. This framework provides i) a theoretical foundation for situating PSM research and individual PSMs, as well as ii) an organization which allows us to characterize method development and selection as a process of navigating through a three-dimensional space (defined by the three components of our framework). Individual moves through this space are specified by means of adapters. In the paper we will illustrate these ideas in detail, with examples taken from parametric design problem solving. 1.

The use of predefined models of problem-solving methods is receiving considerable attention from researchers in the area of knowledge acquisition. Using these models, developers of knowledge-acquisition tools are able to prescribe the roles in which knowledge is used in completing a given task. A number of method-oriented architectures based on a single problem-solving method have been developed by various research groups. Because the methods are domain-independent, method-oriented architectures are limited by the fact that knowledge roles that depend on domain-specific considerations cannot be represented using the model of problem solving. In addition, the interface between the knowledge-acquisition tool and the application expert cannot adequately convey the role of each knowledge type in the task model. PROTG-II is a knowledge-acquisition shell that we are building to generate knowledge-acquisition tools automatically without presupposing a specific model of problem-solving. The shell manages a library of mechanisms---procedures of grain size smaller than that of problem-solving methods. Mechanisms can be combined in PROTG-II to construct problem-solving methods and to define the roles of knowledge that depend on domain considerations. Furthermore, PROTG-II utilizes the concept of adaptation in interfaces to allow the knowledge engineer to produce interfaces that are task- and domain-specific. In this paper, we present the PROTG-II shell and examine the components of its architecture. We also demonstrate the use of PROTG-II with a running example, and discuss the design techniques used to overcome the limitations of method-specific architectures.

Officer praised dog for barking at object." Enables Detect Drugs out FK Initiates Retrieval 5 6 Missing Figure 10. Forgetting to fill the tank with gas A=actual intention; E=expectation; Q=question; C=context; I=index; G=goal Tank Out of Gas Tank Full Tank Low Fill Tank Should have filled up with gas when tank low Expectation What Action to Do? KEY: G = goal; I = index; C = context; Q = question; E = expectation; A = actual intention Results At Store connections with related concepts. Other learning goals take multiple arguments. For instance, a knowledge differentiation goal (Cox & Ram, 1995) is a goal to determine a change in a body of knowledge such that two items are separated conceptually. In contrast, a knowledge reconciliation goal (Cox & Ram, 1995) is one that seeks to merge two items that were mistakenly considered separate entities. Both expansion goals and reconciliation goals may include or spawn a knowledge organization goal (Ram, 1993) that seeks to reorganize the existing knowledge so that it is made available to the reasoner at the appropriate time, as well as modify the structure or content of a concept itself. Such reorganization of knowledge affects the conditions under which a particular piece of knowledge is retrieved or the kinds of indexes associated with an item in memory.

We have for several years been working on an approach to knowledge system building that argues for the existence of a close connection between the tasks which the knowledge system is intended to solve, the methods chosen for them and the vocabulary in which knowledge is to be modeled and represented. We trace the historical origins of the idea that we have called Generic Tasks, and outline their evolution and accomplishments based on them. We then critique their original implementations from the perspective of flexible integration. We follow this with an outline of our current generalization of the view in the form of a theory of task structures. We describe the architectural implications of this view and outline some research directions.

PROTG-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTG-II can be applied to the task of providing protocol-based decision support in the domain of treating HIVinfected patients. For this task, we use a problem-solving method called episodic skeletal-plan refinement. This method is decomposable; we construct it from a set of reusable components. In addition, we build an application ontology that consists of the terms and relations in the domain, plus terms that supply method-specific knowledge requirements. From this ontology, we automatically generate a domain-specific knowledge-acquisition tool. The general goal of the PROTG-II approach is to produce systems and components that are easily maintained and reusable. This is the rationale for constructing a problem-solving method from a set of smaller-grained methods and mechanisms. This is also why our knowledge-acquisition tools are domain-specific and generated automatically from ontologies. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system. Keywords. Decision support; expert systems; knowledge acquisition.