Topic
Knowledge representation and reasoning
About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².
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TL;DR: Several systems adopting this approach to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance are developed.
Abstract: In recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.
235 citations
Book•
01 Feb 1988
TL;DR: This book discussesMeta-Level Extensions of Logic and Machine Learning, a Meta-Level Architecture for Expert Systems, and Applications of Metaknowledge in AI Systems.
Abstract: Checking Proofs in the Metamathematics of First Order Logic (M. Aiello, R. Weyhrauch). Foundations. Issues in Computational Reflection (P. Maes). Meta in Logic (D. Perlis). Meaning in Knowledge Representation (L. Steels). Reasoning by Introspection (K. Konolige). Introspective Fidelity (M. Genesereth). Commonsense Set Theory (D. Perlis). Implementations. Control-Related Meta-Level Facilities in LISP (J. des Rivieres). The Mystery of the Tower Revealed: A Non-Reflective Description of the Reflective Tower (M. Wand, D. Friedman). Communication between LISP and Horn Clauses by Mutual Reflection (R. Ghislanzoni, L. Spampinato, G. Tornielli). The ObjVlisp Kernel: A Reflective Lisp Architecture to Define a Uniform Object-Oriented System (P. Cointe). Conceptual Reflection and Actor Languages (J. Ferber). Evaluation and Reflection in FOL (D. Nardi). OMEGA: An Integrated Reflective Framework (M. Simi, E. Motta). Meta-Levels in SOAR (P. Rosenbloom, J. Laird, A. Newell). Applications. The Uses of Metaknowledge in AI Systems (L. Aiello, G. Levi). Reasoning about Self-Control (J. Batali). A Multi-Context Monotonic Axiomatization of Inessential Non-Monotonicity (F. Giunchiglia, R. Weyhrauch). Declaratively Programmable Interpreters and Meta-Level Inference (B. Welham). A Meta-Level Architecture for Expert Systems (L. Sterling). Object Level Reflection of Inference Rules by Partial Evaluation (P. Coscia et al.). Functional Meta-Level for Logic Programming (P. Mancarella, D. Pedreschi, F. Turini). Meta-Level Extensions of Logic and Machine Learning (P. Brazdil).
232 citations
TL;DR: The main fuzzy approaches for defining spatial relationships including topological (set relationships, adjacency) and metrical relations (distances, directional relative position) are reviewed.
232 citations
Book•
01 Jan 2001
TL;DR: A Reference Model Architecture for Sensory Processing, Value Judgment, and Knowledge Representation for Engineering Unmanned Ground Vehicles and Future Possibilities is presented.
Abstract: Preface. Emergence of a Theory. Knowledge. Perception. Goal Seeking and Planning. A Reference Model Architecture. Behavior Generation. World Modeling, Value Judgment, and Knowledge Representation. Sensory Processing. Engineering Unmanned Ground Vehicles. Future Possibilities. References. Index.
231 citations
01 Jul 2004
TL;DR: In this work, ontologies are proposed for modeling the high-level security requirements and capabilities of Web services and clients and helps to match a client's request with appropriate services-those based on security criteria as well as functional descriptions.
Abstract: Web services will soon handle users' private information. They'll need to provide privacy guarantees to prevent this delicate information from ending up in the wrong hands. More generally, Web services will need to reason about their users' policies that specify who can access private information and under what conditions. These requirements are even more stringent for semantic Web services that exploit the semantic Web to automate their discovery and interaction because they must autonomously decide what information to exchange and how. In our previous work, we proposed ontologies for modeling the high-level security requirements and capabilities of Web services and clients.1 This modeling helps to match a client's request with appropriate services-those based on security criteria as well as functional descriptions.
231 citations