The main effort of the research in knowledge representation is providing theories and systems for expressing structured knowledge and for accessing and reasoning with it in a principled way. Description Logics are considered the most important knowledge representation formalism unifying and giving a logical basis to the well known traditions of Frame-based systems, Semantic Networks and KL-ONE-like languages, Object-Oriented representations, Semantic data models, and Type systems.
Description logics are embodied in several knowledge-based systems and are used to develop various real-life applications. Now in paperback, The Description Logic Handbook provides a thorough account of the subject, covering all aspects of research in this field, namely: theory, implementation, and applications. Its appeal will be broad, ranging from more theoretically oriented readers, to those with more practically oriented interests who need a sound and modern understanding of knowledge representation systems based on description logics. In sum, the book will serve as a unique resource for the subject, and can also be used for self-study or as a reference for knowledge representation and artificial intelligence courses.
Enrico Franconi's Course on Description Logics The material includes slides for 6 modules ( 320 slides): A review of Computational Logics, Structural Description Logics, Propositional Description Logics, Description Logics and Knowledge Bases, Description Logics and Logics, Description Logics and Databases. A web pointer to an online modified version of CRACK, allowing for tracing satisfiability proofs with tableaux, is provided. Enrico Franconi's Course on Description Logics The material includes slides for 6 modules ( 320 slides): A review of Computational Logics, Structural Description Logics, Propositional Description Logics, Description Logics and Knowledge Bases, Description Logics and Logics, Description Logics and Databases. A web pointer to an online modified version of CRACK, allowing for tracing satisfiability proofs with tableaux, is provided.
Two essential tasks in managing description logic knowledge bases are eliminating problematic axioms and incorporating newly formed ones. Standard description logic semantics yields an infinite number of models for DL-Lite knowledge bases, thus it is difficult to develop algorithms for contraction and revision that involve DL models. It is more succinct and importantly, with a finite signature, the semantics always yields a finite number of models. We then define model-based contraction and revision functions for DL-Lite knowledge bases under type semantics and provide representation theorems for them.
Class Description Artificial Intelligence (AI) is a crucial component that complements both the design and complexity of a game. AI brings life to your game(s), but can be surprisingly difficult to get started or even scale. This class aims to teach you the foundations of a basic and scalable state machine AI. You'll understand how to divide your AI into multiple states, run the states accordingly, and add your own custom state(s).
This article surveys previous work on combining planning techniques with expressive representations of knowledge in description logics to reason about tasks, plans, and goals. Description logics can reason about the logical definition of a class and automatically infer class-subclass subsumption relations as well as classify instances into classes based on their definitions. Descriptions of actions, plans, and goals can be exploited during plan generation, plan recognition, or plan evaluation. Another emerging use of these techniques is the semantic web, where current ontology languages based on description logics need to be extended to reason about goals and capabilities for web services and agents.
KL-ONE is a system for representing knowledge in Artificial Intelligence programs. It has been developed and refined over a long period and has been used in both basic research and implemented knowledge-based systems in a number of places in the AI community. Here we present the kernel ideas of KL-ONE, emphasizing its ability to form complex structured descriptions. This research was supported in part by the Defense Advanced Research Projects Agency under Contract N00014-77-C-0378.