Book•
Logical Foundations of Artificial Intelligence
15 Jul 1987-
TL;DR: Typographical Conventions 1 Introduction 1 Bibliographical and Historical Remarks Exercises 2 Declarative Knowledge 2.1 Conceptualization 2.2 Predicate Calculus 2.3 Semantics 2.4 Blocks World Example 2.5 Circuits 2.6 Algebraic Examples 2.7 List Examples 1.9 Specialized Languages 2.8 Reasoning with Uncertain Reasoning 3.1 Probabilities of Sentences 3.4 Provability 3.5 Proving Provability
Abstract: Typographical Conventions 1 Introduction 1.1 Bibliographical and Historical Remarks Exercises 2 Declarative Knowledge 2.1 Conceptualization 2.2 Predicate Calculus 2.3 Semantics 2.4 Blocks World Example 2.5 Circuits Example 2.6 Algebraic Examples 2.7 List Examples 2.8 Natural-Language Examples 2.9 Specialized Languages 2.10 Bibliographical and Historical Remarks Exercises 3 Inference 3.1 Derivability 3.2 Inference Procedures 3.3 Logical Implication 3.4 Provability 3.5 Proving Provability 3.6 Bibliographical and Historical Remarks Exercises 4 Resolution 4.1 Clausal Form 4.2 Unification 4.3 Resolution Principle 4.4 Resolution 4.5 Unsatisfiability 4.6 True-or-False Questions 4.7 Fill-in-the-Blank Questions 4.8 Circuits Example 4.9 Mathematics Example 4.10 Soundness and Completeness 4.11 Resolution and Equality 4.12 Bibliographical and Historical Remarks Exercises 5 Resolution Strategies 5.1 Deletion Strategies 5.2 Unit Resolution 5.3 Input Resolution 5.4 Linear Resolution 5.5 Set of Support Resolution 5.6 Ordered Resolution 5.7 Directed Resolution 5.8 Sequential Constraint Satisfaction 5.9 Bibliographical and Historical Remarks Exercises 6 Nonmonotonic Reasoning 6.1 The Closed-World Assumption 6.2 Predicate Completion 6.3 Taxonomic Hierarchies and Default Reasoning 6.4 Circumscription 6.5 More General Forms of Circumscription 6.6 Default Theories 6.7 Bibliographical and Historical Remarks Exercises 7 Induction 7.1 Induction 7.2 Concept Formation 7.3 Experiment Generation 7.4 Bibliographical and Historical Remarks Exercises 8 Reasoning with Uncertain Beliefs 8.1 Probabilities of Sentences 8.2 Using Bayes' Rule in Uncertain Reasoning 8.3 Uncertain Reasoning in Expert Systems 8.4 Probabilistic Logic 8.5 Probabilistic Entailment 8.6 Computations Appropriate for Small Matrices 8.7 Dealing with Large Matrices 8.8 Probabilities Conditioned on Specific Information 8.9 Bibliographical and Historical Remarks Exercises 9 Knowledge and Belief 9.1 Preliminaries 9.2 Sentential Logics of Belief 9.3 Proof Methods 9.4 Nested Beliefs 9.5 Quantifying-In 9.6 Proof Methods for Quantified Beliefs 9.7 Knowing What Something Is 9.8 Possible-Worlds Logics 9.9 Properties of Knowledge 9.10 Properties of Belief 9.11 Group Knowledge 9.12 Equality, Quantification, and Knowledge 9.13 Bibliographical and Historical Remarks Exercises 10 Metaknowledge and Metareasoning 10.1 Metalanguage 10.2 Clausal Form 10.3 Resolution Principle 10.4 Inference Procedures 10.5 Derivability and Belief 10.6 Metalevel Reasoning 10.7 Bilevel Reasoning 10.8 Reflection 10.9 Bibliographical and Historical Remarks Exercises 11 State and Change 11.1 States 11.2 Actions 11.3 The Frame Problem 11.4 Action Ordering 11.5 Conditionality 11.6 Bibliographical and Historical Remarks Exercises 12 Planning 12.1 Initial State 12.2 Goals 12.3 Actions 12.4 Plans 12.5 Green's Method 12.6 Action Blocks 12.7 Conditional Plans 12.8 Planning Direction 12.9 Unachievability Pruning 12.10 State Alignment 12.11 Frame-Axiom Suppression 12.12 Goal Regression 12.13 State Differences 12.14 Bibliographical and Historical Remarks Exercises 13 Intelligent-Agent Architecture 13.1 Tropistic Agents 13.2 Hysteretic Agents 13.3 Knowledge-Level Agents 13.4 Stepped Knowledge-Level Agents 13.5 Fidelity 13.6 Deliberate Agents 13.7 Bibliographical and Historical Remarks Exercises Answers to Exercises A.1 Introduction A.2 Declarative Knowledge A.3 Inference A.4 Resolution A.5 Resolution Strategies A.6 Nonmonotonic Reasoning A.7 Induction A.8 Reasoning with Uncertain Beliefs A.9 Knowledge and Belief A.10 Metaknowledge and Metareasoning A.11 State and Change A.12 Planning A.13 Intelligent-Agent Architecture References Index
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01 Jan 1988TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Abstract: From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
15,671 citations
Book•
01 Jan 1992TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Abstract: Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository
13,487 citations
TL;DR: This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.
12,962 citations
Cites background from "Logical Foundations of Artificial I..."
...Introduction A body of formally represented knowledge is based on a conceptualization: the objects, concepts, and other entities that are presumed to exist in some area of interest and the relationships that hold them (Genesereth & Nilsson, 1987)....
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...One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content....
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01 Dec 1995-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: The role of ontology in supporting knowledge sharing activities is described, and a set of criteria to guide the development of ontologies for these purposes are presented, and it is shown how these criteria are applied in case studies from the design ofOntologies for engineering mathematics and bibliographic data.
Abstract: 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.
6,949 citations
Cites background from "Logical Foundations of Artificial I..."
...A body of formally represented knowledge is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them (Genesereth & Nilsson, 1987)....
[...]
...Like conventional applications, knowledge-based systems are based on heterogeneous hardware platforms, programming languages, and network protocols....
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TL;DR: 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 as discussed by the authors ; agent architectures can be thought of as software engineering models of agents; and agent languages are software systems for programming and experimenting with agents.
Abstract: 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.
6,714 citations