Representations Of Commonsense Knowledge
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| Author | : Ernest Davis |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 540 |
| Release | : 2014-07-10 |
| Genre | : Computers |
| ISBN | : 148322113X |
Representations of Commonsense Knowledge provides a rich language for expressing commonsense knowledge and inference techniques for carrying out commonsense knowledge. This book provides a survey of the research on commonsense knowledge. Organized into 10 chapters, this book begins with an overview of the basic ideas on artificial intelligence commonsense reasoning. This text then examines the structure of logic, which is roughly analogous to that of a programming language. Other chapters describe how rules of universal validity can be applied to facts known with absolute certainty to deduce other facts known with absolute certainty. This book discusses as well some prominent issues in plausible inference. The final chapter deals with commonsense knowledge about the interrelations and interactions among agents and discusses some issues in human and social interactions that have been studied in the artificial intelligence literature. This book is a valuable resource for students on a graduate course on knowledge representation.
| Author | : Ronald Brachman |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 414 |
| Release | : 2004-05-19 |
| Genre | : Computers |
| ISBN | : 1558609326 |
Knowledge representation is at the very core of a radical idea for understanding intelligence. This book talks about the central concepts of knowledge representation developed over the years. It is suitable for researchers and practitioners in database management, information retrieval, object-oriented systems and artificial intelligence.
| Author | : Erik T. Mueller |
| Publisher | : Elsevier |
| Total Pages | : 431 |
| Release | : 2010-07-26 |
| Genre | : Computers |
| ISBN | : 0080476619 |
To endow computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik T. Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringing into one place information scattered across many books and papers. Mueller shares the knowledge gained in using the event calculus and extends the literature with detailed event calculus solutions to problems that span many areas of the commonsense world. Covers key areas of commonsense reasoning including action, change, defaults, space, and mental states. The first full book on commonsense reasoning to use the event calculus. Contextualizes the event calculus within the framework of commonsense reasoning, introducing the event calculus as the best method overall. Focuses on how to use the event calculus formalism to perform commonsense reasoning, while existing papers and books examine the formalisms themselves. Includes fully worked out proofs and circumscriptions for every example.
| Author | : Mircea Gh. Negoita |
| Publisher | : Springer |
| Total Pages | : 962 |
| Release | : 2004-10-14 |
| Genre | : Computers |
| ISBN | : 3540301348 |
We were very pleased to once again extend to the delegates and, we are pleased to th say, our friends the warmest of welcomes to the 8 International Conference on Knowledge-Based Intelligent Information and Engineering Systems at Wellington - stitute of Technology in Wellington, New Zealand. The KES conferences attract a wide range of interest. The broad focus of the c- ference series is the theory and applications of computational intelligence and em- gent technologies. Once purely a research field, intelligent systems have advanced to the point where their abilities have been incorporated into many conventional appli- tion areas. The quest to encapsulate human knowledge and capabilities in domains such as reasoning, problem solving, sensory analysis, and other complex areas has been avidly pursued. This is because it has been demonstrated that these abilities have definite practical applications. The techniques long ago reached the point where they are being exploited to provide commercial advantages for companies and real beneficial effects on profits. KES 2004 provided a valuable mechanism for delegates to obtain a profound view of the latest intelligent systems research into a range of - gorithms, tools and techniques. KES 2004 also gave delegates the chance to come into contact with those applying intelligent systems in diverse commercial areas. The combination of theory and practice represents a uniquely valuable opportunity for - preciating the full spectrum of intelligent-systems activity and the “state of the art”.
| Author | : Abbas Z. Kouzani |
| Publisher | : |
| Total Pages | : |
| Release | : |
| Genre | : |
| ISBN | : |
| Author | : Frank van Harmelen |
| Publisher | : Elsevier |
| Total Pages | : 1035 |
| Release | : 2008-01-08 |
| Genre | : Computers |
| ISBN | : 0080557023 |
Handbook of Knowledge Representation describes the essential foundations of Knowledge Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field. It includes a tutorial background and cutting-edge developments, as well as applications of Knowledge Representation in a variety of AI systems. This handbook is organized into three parts. Part I deals with general methods in Knowledge Representation and reasoning and covers such topics as classical logic in Knowledge Representation; satisfiability solvers; description logics; constraint programming; conceptual graphs; nonmonotonic reasoning; model-based problem solving; and Bayesian networks. Part II focuses on classes of knowledge and specialized representations, with chapters on temporal representation and reasoning; spatial and physical reasoning; reasoning about knowledge and belief; temporal action logics; and nonmonotonic causal logic. Part III discusses Knowledge Representation in applications such as question answering; the semantic web; automated planning; cognitive robotics; multi-agent systems; and knowledge engineering. This book is an essential resource for graduate students, researchers, and practitioners in knowledge representation and AI. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily
| Author | : Antoine Bosselut |
| Publisher | : |
| Total Pages | : 154 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
For machines to understand language, they must intuitively grasp the commonsense knowledge that underlies the situations they encounter in text. A simple statement such as it is raining immediately implies a bank of shared context for any human reader: they should bring an umbrella, roads will be slippery, increased traffic may make them late, rain boots are preferable to sandals, and many more. Language understanding systems must be able to robustly use this commonsense knowledge to make decisions or take actions. Observations of the world are always more rich and detailed than the information that is explicitly transmitted through language, and machines must be able to fill in remaining details with commonsense inferences. Recent advances in natural language processing have made considerable progress in identifying the commonsense implications of situations described in text. These methods generally involve training high-parameter language models on large language corpora and have shown marked improvement on a variety of benchmark end tasks in natural language understanding. However, these systems are brittle 0́3 often failing when presented with out-of-distribution inputs -- and uninterpretable -- incapable of providing insights into why these different inputs cause shifted behavior. Meanwhile, traditional approaches to natural language understanding, which focus on linking language to background knowledge from large ontologies, remain limited by their inability to scale to the situational diversity expressed through language. In this dissertation, we argue that for natural language understanding agents to function in less controlled test environments, they must learn to reason more explicitly about the commonsense knowledge underlying textual situations. In furtherance of these goals, we draw from both traditional symbolic and modern neural approaches to natural language understanding. We present four studies on learning commonsense representations from language, and integrating and reasoning about these representations in NLP systems to achieve more robust textual understanding.
| Author | : SRI International. Artificial Intelligence Center |
| Publisher | : |
| Total Pages | : 17 |
| Release | : 1982 |
| Genre | : |
| ISBN | : |
| Author | : Nick Cercone |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 545 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461247926 |
Knowledge representation is perhaps the most central problem confronting artificial intelligence. Expert systems need knowledge of their domain of expertise in order to function properly. Computer vlslOn systems need to know characteristics of what they are "seeing" in order to be able to fully interpret scenes. Natural language systems are invaluably aided by knowledge of the subject of the natural language discourse and knowledge of the participants in the discourse. Knowledge can guide learning systems towards better understanding and can aid problem solving systems in creating plans to solve various problems. Applications such as intelligent tutoring. computer-aided VLSI design. game playing. automatic programming. medical reasoning. diagnosis in various domains. and speech recogOltlOn. to name a few. are all currently experimenting with knowledge-based approaches. The problem of knowledge representation breaks down into several subsidiary problems including what knowledge to represent in a particular application. how to extract or create that knowledge. how to represent the knowledge efficiently and effectively. how to implement the knowledge representation scheme chosen. how to modify the knowledge in the face of a changing world. how to reason with the knowledge. and how tc use the knowledge appropriately in the creation of the application solution. This volume contains an elaboration of many of these basic issues from a variety of perspectives.
| Author | : SRI International. Artificial Intelligence Center |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 1982 |
| Genre | : |
| ISBN | : |
This paper examines the role that formal logic ought to play in representing and reasoning with commonsense knowledge, We take issue with the commonly held view (as expressed by Newell [1980)) that the use of representations based on formal logic is inappropriate in most applications of artificial intelligence. We argue to the contrary that there is an important set of issues, involving incomplete knowledge of a problem situation, that so far have been addressed only by systems b)ased on formal logic and deductive inference, and that, in some sense, probably can be dealt with only by systems based on logic and deduction. We further argue that the experiments of the late l960s on problem- solving by theorem-proving did not show that the use of logic and deduction in AI systems was necessarily inefficient, but rather that what was needed was better control of the deduction process, combined with more attention to the computational properties of axioms.
