A translation approach to portable ontology specifications

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.

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Toward principles for the design of ontologies used for knowledge sharing

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

The software world is one of great richness and diversity. Many thousands of software products are available to users today, providing a wide variety of information and services in a wide variety of domains. While most of these programs provide their users with significant value when used in isolation, there is increasing demand for programs that can interoperate – to exchange information and services with other programs and thereby solve problems that cannot be solved alone. Part of what makes interoperation difficult is heterogeneity. Programs are written by different people, at different times, in different languages; and, as a result, they often provide different interfaces. The difficulties created by heterogeneity are exacerbated by dynamics in the software environment. Programs are frequently rewritten; new programs are added; old programs removed. Agent-based software engineering was invented to facilitate the creation of software able to interoperate in such settings. In this approach to software development, application programs are written as software agents, i.e. software “components” that communicate with their peers by exchanging messages in an expressive agent communication language. Agents can be as simple as subroutines; but typically they are larger entities with some sort of persistent control (e.g. distinct control threads within a single address space, distinct processes on a single machine, or separate processes on different machines). The salient feature of the language used by agents is its expressiveness. It allows for the exchange of data and logical information, individual commands and scripts (i.e. programs). Using this language, agents can communicate complex information and goals, directly or indirectly “programming” each other in useful ways. Agent-based software engineering is often compared to object-oriented programming. Like an “object”, an agent provides a message-based interface independent of its internal data structures and algorithms. The primary difference between the two approaches lies in the language of the interface. In general object-oriented programming, the meaning of a message can vary from one object to another. In agent-based software engineering, agents use a common language with an agent-independent semantics. The concept of agent-based software engineering raises a number of important questions.

Software agents

KQML as an agent communication language

The Palo Alto Collaborative Testbed (PACT), a concurrent engineering infrastructure that encompasses multiple sites, subsystems, and disciplines, is discussed. The PACT systems include NVisage, a distributed knowledge-based integration environment for design tools; DME (Device Modeling Environment), a model formulation and simulation environment; Next-Cut, a mechanical design and process planning system; and Designworld, a digital electronics design, simulation, assembly, and testing system. The motivations for PACT and the significance of the approach for concurrent engineering is discussed. Initial experiments in distributed simulation and incremental redesign are reviewed, and PACT's agent-based architecture and lessons learned from the PACT experiments are described. >

PACT: an experiment in integrating concurrent engineering systems

The ability to obtain relevant information in a timely and cost efficient manner is central to the effective performance of most tasks in our society. The widespread availability of computer-based information brokers will provide that ability to large communities by significantly facilitating effective access to the broad range of information that is rapidly becoming available on the Internet. The general availability of the technology to build and maintain information brokers will enable an industry to be established whose primary products are computer-based network-accessible brokering services.

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Network-Based Information Brokers

In this paper, we provide a practical framework for characterizing, evaluating and selecting reformulation techniques for reasoning about physical systems, with the long-term goal of automating the selection and application of these techniques. We view reformulation as a mapping from one encoding of a problem to another. A problem solving task is in turn accomplished by the application of a sequence of reformulations to an initial problem encoding to produce a final encoding that addresses the task. Our framework provides the terminology to specify the conditions under which a particular reformulation technique is applicable, the cost associated with performing the reformulation, and the effects of the reformulation with respect to the problem encoding. As such it provides the vocabulary to characterize the selection of a sequence of reformulation techniques as a planning problem. Our framework is sufficiently flexible to accommodate previously proposed properties and metrics for reformulation. We have used the framework to characterize a variety of reformulation techniques, three of which are presented in this paper.

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Preliminary Thoughts Towards a Practical Theory of Reformulation for Reasoning about Physical Systems

Ontolingua, a language for ontology-based knowledge representation, provides the capability to construct comprehensive characterizations of knowledge bases. While the ability to characterize the content of a knowledge base is not new, Ontolingua includes a number of features that greatly enhance conventional data representation and modeling technologies through the incorporation of semantic context. In addition to supporting object-oriented modeling techniques, Ontolingua enables representation of constraints, definitions, and relationships among terms within ontologies. This facility provides a framework that supports automated translation among knowledge bases with differing data models and physical implementations. The ability to formally describe and unambiguously distinguish between diverse data sources is essential to enabling reuse of intellectual property. This paper presents a high-level view of ontology-based knowledge representation and an approach to solving the intellectual property reuse problem through the application of this technology.

Robert Engelmore

Papers

PACT: an experiment in integrating concurrent engineering systems

Network-Based Information Brokers

Preliminary Thoughts Towards a Practical Theory of Reformulation for Reasoning about Physical Systems

Application of Ontology-Based Knowledge Representation to Design Reuse

Thoughts Towards a Practical Theory of Reformulation for Reasoning about Physical Systems