Steps toward a theoretic foundation for complex knowledge-based CAI.

In this book we have presented experimental evidence at many levels of detail for a diverse set of hypotheses. As indicated by the chapter and section headings, the major themes of the MYCIN work have many variations. In this final chapter we will try to summarize the most important results of the work presented. This recapitulation of the lessons learned should not be taken as a substitute for details in the sections themselves. We provide here an abstraction of the details, but hope it also constitutes a useful set of lessons on which others can build. The three main sections of this chapter will reiterate the main goals that provide the context for the experimental work; discuss the experimental results from each of the major parts of the book; and summarize the key questions we have been asked, or have asked ourselves, about the lessons we have learned. If we were to try to summarize in one word why MYCIN works as well as it does, that word would be flexibility.

Here we consider the logical bases for rules: what kinds of arguments justify the rules, and what is their relation to a mechanistic model of the domain? We use the terms "explain" and "justify" synonymously, although the sense of "making clear what is not understood" (explain) is intended more than "vindicating, showing to be right or lawful" (justify).

This chapter explores a number of issues involving representation and use of what we term meta-level knowledge, or knowledge about knowledge.1 It begins by defining the term, then exploring a few of its varieties and considering the range of capabilities it makes possible. Four specific examples of meta-level knowledge are described, and a demonstration given of their application to a number of problems, including interactive transfer of expertise and the "intelligent" use of knowledge. Finally, we consider the long-term implications of the concept and its likely impact on the design of large programs. The context of this work is the TEIRESIAS program discussed in Chapter 9. In the earlier chapter we focused on the use of TEIRESIAS for knowledge acquisition. Here we focus on the classification and types of knowledge used by TEIRESIAS. In the most general terms, meta-level knowledge is knowledge about knowledge. Its primary use here is to enable a program to "know what it knows," and to make multiple uses of its knowledge. As mentioned in Chapter 9, the program is not only able to use its knowledge directly, but may also be able to examine it, abstract it, reason about it, or direct its application. This chapter discusses examples of meta-level knowledge classified along two dimensions: (i) specificity character (representation-specific vs. domain-specific), and (ii) source (user-supplied vs. derived). Representation-specific meta-level knowledge involves supplying a program with a store of knowledge dealing with the form of its representations, in particular, their design and organization. Traditionally, this design and organization infor-This chapter is an expanded and edited version of a paper originally appearing in Proceedings of the Fifth IJCAL 1977, pp. Used by permission of International Joint Conferences on Artificial Intelligence, Inc.; copies of the Proceedings are available from William Kaufmann, Inc., 95 First Street, Los Altos, CA 94022. IFollowing standard usage, knowledge about objects and relations in a particular domain will be referred to as object-level knowledge. Type declarations are a small step toward more explicit specification of this information, especially as they are used in extended data types and record structures. As we discuss below, this sort of information, along with a range of other facts about representation design, can be employed quite usefully if it is made explicit and made available to the system.

We have found that the feature is therefbre seldom used. It would, of course, be preferable if the system "knew" on its own when such a summary is appropriate.

IDijkstra 19591 Dijkstra, E., NA Note on Two Problems in Connection with Graphs," Numeri.scht'

It is no pure coincidence that an organic chemist rather than a computer specialist is writing this foreword.

Harold Shephard Samuel D. Thurman William T. Lake JOINDER OF CLAIMS, COUNTERCLAIMS, AND CROSS-COMPLAINTS: SUGGESTED REVISION OF THE CALIFORNIA PROVISIONS. Research in artificial intelligence, a branch of computer science, has illuminated our capacity to use computers to model human thought processes. In this Article we will argue that the time has come for serious interdisciplinary work between lawyers and computer scientists to explore the computer's potential in law. Interdisciplinary work between the lawyer and the computer scientist has floundered on the misconceptions that each has of the other's discipline. As a result, no one has yet attempted computer programs incorporating complex techniques of legal reasoning. Even efforts in legal information retrieval have been hampered by these misconceptions. In retrieval, lawyers have viewed the computer as, at most, a storehouse from which cases and statutes might be retrieved by skillfully designed indexing systems.

While in classical planning the objective is to achieve one of the equally attractive goal states at as low total action cost as possible, the objective in deterministic oversubscription planning (OSP) is to achieve an as valuable as possible subset of goals within a fixed allowance of the total action cost. Although numerous applications in various fields share the latter objective, no substantial algorithmic advances have been made in deterministic OSP. Tracing the key sources of progress in classical planning, we identify a severe lack of effective domain-independent approximations for OSP. With our focus here on optimal planning, our goal is to bridge this gap. Two classes of approximation techniques have been found especially useful in the context of optimal classical planning: those based on state-space abstractions and those based on logical landmarks for goal reachability. The question we study here is whether some similar-in-spirit, yet possibly mathematically different, approximation techniques can be developed for OSP. In the context of abstractions, we define the notion of additive abstractions for OSP, study the complexity of deriving effective abstractions from a rich space of hypotheses, and reveal some substantial, empirically relevant islands of tractability. In the context of landmarks, we show how standard goal-reachability landmarks of certain classical planning tasks can be compiled into the OSP task of interest, resulting in an equivalent OSP task with a lower cost allowance, and thus with a smaller search space. Our empirical evaluation confirms the effectiveness of the proposed techniques, and opens a wide gate for further developments in oversubscription planning.

--This article proposes the use of V ector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The adoption of a V ector Symbolic representation ensures a one-layered design for the approach, while maintaining the previously reported properties and performance characteristics of Hierarchical Graph Neuron, and also improving the noise resistance of the architecture. The proposed architecture enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern. RAPH Neuron (GN) is an approach for memorizing patterns of generic sensor stimuli for later template matching. It is based on the hypothesis that a better associative memory resource can be created by changing the emphasis from high speed sequential CPU processing to parallel network-centric processing [2], [3]. In contrast to contemporary machine learning approaches, GN allows introduction of new patterns in the learning set without the need for retraining. Whilst doing so, it exhibits a high level of scalability i.e. its performance and accuracy do not degrade as the number of stored patterns increases over time. V ector Symbolic Architectures (VSA) [4] are a bio-inspired method of representing concepts and their meaning for modeling cognitive reasoning. It exhibits a set of unique properties which make it suitable for implementation of artificial general intelligence [5], [6], [7], and so, creation of complex systems for sensing and pattern recognition without reliance on complex computation. In the biological world, extremely successful applications of these approaches can be found.