Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focus on what an agent needs to know in order to behave intelligently, how this knowledge can be represented symbolically, and how automated reasoning procedures can make this knowledge available as needed. 

This landmark text takes the central concepts of knowledge representation developed over the last 50 years and illustrates them in a lucid and compelling way. Each of the various styles of representation is presented in a simple and intuitive form, and the basics of reasoning with that representation are explained in detail. This approach gives readers a solid foundation for understanding the more advanced work found in the research literature. The presentation is clear enough to be accessible to a broad audience, including researchers and practitioners in database management, information retrieval, and object-oriented systems as well as artificial intelligence. This book provides the foundation in knowledge representation and reasoning that every AI practitioner needs.

*Authors are well-recognized experts in the field who have applied the techniques to real-world problems 
* Presents the core ideas of KR&R in a simple straight forward approach, independent of the quirks of research systems 
*Offers the first true synthesis of the field in over a decade

Table of Contents

1 Introduction * 2 The Language of First-Order Logic *3 Expressing Knowledge * 4 Resolution * 5 Horn Logic * 6 Procedural Control of Reasoning * 7 Rules in Production Systems * 8 Object-Oriented Representation * 9 Structured Descriptions * 10 Inheritance * 11 Numerical Uncertainty *12 Defaults *13 Abductive Reasoning *14 Actions * 15 Planning *16 A Knowledge Representation Tradeoff * Bibliography * Index

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Knowledge Representation and Reasoning

Characterizing diagnoses and systems

The paper considers two decision problems on hypergraphs, hypergraph saturation and recognition of the transversal hypergraph, and discusses their significance for several search problems in applied computer science. Hypergraph saturation (i.e., given a hypergraph $\cal H$, decide if every subset of vertices is contained in or contains some edge of $\cal H$) is shown to be co-NP-complete. A certain subproblem of hypergraph saturation, the saturation of simple hypergraphs (i.e., Sperner families), is shown to be under polynomial transformation equivalent to transversal hypergraph recognition; i.e., given two hypergraphs ${\cal H}_{1}, {\cal H}_{2}$, decide if the sets in ${\cal H}_{2}$ are all the minimal transversals of ${\cal H}_{1}$. The complexity of the search problem related to the recognition of the transversal hypergraph, the computation of the transversal hypergraph, is an open problem. This task needs time exponential in the input size; it is unknown whether an output-polynomial algorithm exists. For several important subcases, for instance if an upper or lower bound is imposed on the edge size or for acyclic hypergraphs, output-polynomial algorithms are presented. Computing or recognizing the minimal transversals of a hypergraph is a frequent problem in practice, which is pointed out by identifying important applications in database theory, Boolean switching theory, logic, and artificial intelligence (AI), particularly in model-based diagnosis.

Identifying the Minimal Transversals of a Hypergraph and Related Problems

In many problem domains, solutions take the form of unordered sets. We present the Set-Enumerations (SE)tree a vehicle for representing sets and/or enumerating them in a best-first fashion. We demonstrate its usefulness as the basis for a unifying search-based framework for domains where minimal (maximal) elements of a power set are targeted, where minimal (maximal) partial instantiations of a set of variables are sought, or where a composite decision is not dependent on the order in which its primitive component-decisions are taken. Particular instantiations of SE-tree-based algorithms for some AI problem domains are used to demonstrate the general features of the approach. These algorithms are compared theoretically and empirically with current algorithms. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-92-66. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/297 Search Through Systematic Set Enumeration MS-CIS-92-66 LINC LAB 234

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Search through systematic set enumeration

Linear resolution for consequence finding

An incremental method for generating prime implicants/implicates

A {\em truth maintenance system} is a subsystem that manages the utilization of assumptions in the reasoning process of a problem solver. Doyle''s original motivation for creating a truth maintenance system was to augment a reasoning system with a control strategy for activities concerning its non-monotonic state of beliefs. Hitherto, much effort has been invested in designing and implementing the concept of truth maintenance and little effort has been dedicated to the formalization that is essential to understanding it. This paper provides a complete formalization of the principle of truth maintenance. Motivated by Reiter and de Kleer''s preliminary report on the same subject, this paper extends their study and gives a formal account of the concept of truth maintenance under the general title of {\em assumption based reasoning}. The concept of assumption based theory is defined and the notions of explanation and direct consequence are presented as forms of plausible conclusion with respect to this theory. Additionally, the concept of extension and irrefutable sentences are discussed together with other variations of explanation and direct consequence. A set of algorithms for computing these conclusions for a given theory are presented using the notion of prime implicates. Finally, an extended example on Boolean circuit diagnosis is shown to examplify these ideas.

Assumption Based Reasoning and Clause Management Systems

Most students see exams as a stressful experience, having crammed as much as they possibly can and hoping that the material they have studied would indeed be on the exams, and the material that they did not have the time to study for, nor understand, would be overlooked by their professors. At the same time, instructors see exams as formal assessments of student learning. Exams are seldom thought of as a learning experience. In this paper, we report our experiences in the use of two-stage exams as a learning experience for the students in two different courses. In a two-stage exam, students write an exam individually, then they rewrite the same exam in collaboration with three or four other students. One of the main objectives is to encourage peer instruction and promote learning even during the exams. Students obtain immediate feedback during the exam, and initial results have shown that this type of exam format produces some positive effects in later assessment.

Turning exams into a learning experience

This paper provides an extension to the study of the clause management system (CMS) proposed by Reiter and de Kleer. The CMS is adapted specifically for aiding a reasoning system in explanations generation. The reasoning system transmits propositional formulae representing its knowledge to the CMS and in return, it requests the CMS for minimal and consistent explanations of a query with respect to the CMS knowledge base. The CMS knowledge base is represented by a set of prime implicates. The classification of implicates as minimal, prime, trivial, and minimal trivial is carefully examined. Similarly, the notion of a support for a clause including minimal, prime, trivial, and minimal trivial is also elaborated. The methods to compute these supports from implicates and a preference ordering scheme expressible by logical specificity for the set of supports for a given clause are also presented. The generalization of the notion of a minimal support for a conjunction of clauses is also shown. Finally, two logic-based diagnostic reasoning paradigms aided by the CMS are shown to exemplify the functionality of the CMS.

George K. Tsiknis

Papers

An incremental method for generating prime implicants/implicates

Assumption Based Reasoning and Clause Management Systems

Turning exams into a learning experience

Clause management systems (CMS)

Logical Foundations for Programming Semantics