In this article, we develop a framework for comparing ontologies and place a number of the more prominent ontologies into it. We have selected 10 specific projects for this study, including general ontologies, domain-specific ones, and one knowledge representation system. The comparison framework includes general characteristics, such as the purpose of an ontology, its coverage (general or domain specific), its size, and the formalism used. It also includes the design process used in creating an ontology and the methods used to evaluate it. Characteristics that describe the content of an ontology include taxonomic organization, types of concept covered, top-level divisions, internal structure of concepts, representation of part-whole relations, and the presence and nature of additional axioms. Finally, we consider what experiments or applications have used the ontologies. Knowledge sharing and reuse will require a common framework to support interoperability of independently created ontologies. Our study shows there is great diversity in the way ontologies are designed and the way they represent the world. By identifying the similarities and differences among existing ontologies, we clarify the range of alternatives in creating a standard framework for ontology design.

There is a great dispute among researchers about the roles, characteristics, and specifications of what are called agents, intelligent agents, and adaptive agents. Most research in the field focuses on methodologies for solving specific problems (for example, communications, cooperation, architectures), and little work has been accomplished to highlight and distinguish the field of intelligent agents. As a result, more and more research is cataloged as research on intelligent agents. Therefore, it was necessary to bring together researchers working in the field to define initial boundaries, criteria, and acceptable characteristics of the field. The Workshop on Intelligent Adaptive Agents, presented as part of the Thirteenth National Conference on Artificial Intelligence, addressed these issues as well as many others that are presented in this article.

With a power screwdriver the computer, the web, robots, the Europe the Hindu-Arabic system of anyone can drive the hardest screw; automation of manufacturing will all numbers and the arithmetic algorithms with a calculator, anyone can get the conspire to separate the rich and they made possible. One of the numbers right; with an aircraft anyone quick from the poor and slow, hurrying first books after the Bible printed with can fly to Paris; and with Deep the trend to an informed, skilled, moveable type was an Arithmetic. Blue, anyone can beat the world chess and employed elite living among an Even so, the algorithms were not easy champion. Cognitive prostheses undermine uninformed, unskilled, and unemployed and not widely disseminated. But both history and 17th century tradesman could not by giving non-experts equivalent an understanding of human-machine multiply.

It comprises activities Systems, Knowledge Representation managing an interaction. On the focused on the organization, acquiring and Reasoning, and Knowledge Discovery. Wide Web and will remain available in any system that aims to tutor users The KM community has been at ksi.cpsc.ucalgary.ca/AIKM97. Cross-language text retrieval (CLTR) is the problem of matching a query in The presentations made at this symposium This symposium brought together one language to related documents in dealt with vastly different environments, researchers in natural language processing other languages. As internet resources ranging from digital libraries (NLP) from both academia such as the World Wide Web have and broadcast news archives to virtual and industry, including service become global networks, many new reality and, of course, the web.

Does machine learning really work? Yes. Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value. Machine-learning algorithms have now learned to detect credit card fraud by mining data on past transactions, learned to steer vehicles driving autonomously on public highways at 70 miles an hour, and learned the reading interests of many individuals to assemble personally customized electronic newsAbstracts. A new computational theory of learning is beginning to shed light on fundamental issues, such as the trade-off among the number of training examples available, the number of hypotheses considered, and the likely accuracy of the learned hypothesis. Newer research is beginning to explore issues such as long-term learning of new representations, the integration of Bayesian inference and induction, and life-long cumulative learning. This article, based on the keynote talk presented at the Thirteenth National Conference on Artificial Intelligence, samples a number of recent accomplishments in machine learning and looks at where the field might be headed. [Copyright restrictions preclude electronic publication of this article.]

Interestingly, the advantage for more effective access to traditional applications of agent of modeling others is diminished global and corporate information modeling, which requires an agent to when the volatility of the domain is repositories. These repositories are model the problem-solving processes low. Thus, the models of other agents increasingly multimedia, including of the interacting human to provide are more useful in variable domains, text, audio, graphics, imagery, and video. Now attention has turned appropriate feedback. Ole Mengshoel when they are a particularly valuable guide to predict what the other rational toward the problem of processing and D. C. Wilkins's (both of University agents are going to do. and managing multiple and heterogeneous of Illinois at Urbana-Champaign) media in a principled manner, presentation focused on recognizing

Special Information about the conference The 1998 Program Committee invites thanks are extended to Barbara Grosz, is available by writing to ncai@aaai.

Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.

SEQUITUR is an algorithm that infers a hierarchical structure from a sequence of discrete symbols by replacing repeated phrases with a grammatical rule that generates the phrase, and continuing this process recursively. The result is a hierarchical representation of the original sequence, which offers insights into its lexical structure. The algorithm is driven by two constraints that reduce the size of the grammar, and produce structure as a by-product. SEQUITUR breaks new ground by operating incrementally. Moreover, the method's simple structure permits a proof that it operates in space and time that is linear in the size of the input. Our implementation can process 50,000 symbols per second and has been applied to an extensive range of real world sequences.

This paper combines two important directions of research in temporal resoning: that of finding maximal tractable subclasses of Allen's interval algebra, and that of reasoning with metric temporal information. Eight new maximal tractable subclasses of Allen's interval algebra are presented, some of them subsuming previously reported tractable algebras. The algebras allow for metric temporal constraints on interval starting or ending points, using the recent framework of Horn DLRs. Two of the algebras can express the notion of sequentiality between intervals, being the first such algebras admitting both qualitative and metric time.