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@헥터레베스크 Hector Levesque #지식표현 #추론분야 #인공지능 #석학

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The Logic of Knowledge Bases

(헥터 레베스크 2023)

  • 헥터 레베스크

  • The idea of a knowledge base lies at the heart of symbolic or “good old-fashioned” artificial intelligence (GOFAI). A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge, its knowledge base. The system is not programmed for specific tasks; rather, it is told what it needs to know, and expected to infer the rest. This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way, the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a rigorous mathematical model of knowledge that is general and expressive, yet more workable in practice than previous models. The first edition of the book appeared in the year 2000, and since then its model of knowledge has been applied and extended in a number of ways. This second edition incorporates a number of new results about the logic of knowledge bases, including default reasoning, reasoning about action and change, and tractable reasoning. Hector Levesque is Professor Emeritus in the Department of Computer Science, University of Toronto. Gerhard Lakemeyer is Professor and Chair of the Department of Computer Science, RWTH Aachen University, and Professor (status only) in the Department of Computer Science, University of Toronto.

  • 2023

Common Sense, the Turing Test, and the Quest for Real AI

(헥터 레베스크 2018)

  • 헥터 레베스크

  • What artificial intelligence can tell us about the mind and intelligent behavior.What can artificial intelligence teach us about the mind? If AI’s underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It’s a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to “good old fashioned artificial intelligence,” which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns—as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there’s no more soy milk. Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence—the Winograd Schema Test, developed by Levesque and his colleagues. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.

  • 2018

Thinking as Computation: A First Course

(헥터 레베스크 2017)

  • Thinking as Computation
  • 헥터 레베스크
  • Students explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought. This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog (without having to learn algorithms: “Prolog without tears!”), learning to express what they need as a Prolog program and letting Prolog search for answers.After an introduction to the basic concepts, Thinking as Computation offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises. The book is based on a popular course at the University of Toronto and can be used in a variety of classroom contexts, by students ranging from first-year liberal arts undergraduates to more technically advanced computer science students.
  • 2017

Machines like Us: Toward AI with Common Sense

(헥터 레베스크 and Brachman 2022)

  • Machines like Us
  • 헥터 레베스크 and Brachman, Ronald J.
  • How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise.It’s sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what’s happening and find a workaround. In Machines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today’s AI systems. Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.
  • 2022

헥터 레베스크 “Hector Levesque #지식표현 #추론분야 #인공지능”

(헥터 레베스크 1951)

#에릭랄슨: #인공지능 #신화 컴퓨터가 우리가 하는 방식으로 생각할 수 없는 이유

My research is in the area of knowledge representation and reasoning in artificial intelligence. 저의 연구는 지식 표현 및 추론 분야입니다. 인공 지능.

On the representation side, I’ve worked on the formalization of a number of concepts pertaining to artificial and natural agents including belief, goals, intentions, ability, and the interaction between knowledge, perception and action. 대표성 측면에서 저는 인공 및 자연과 관련된 여러 개념의 공식화 신념, 목표, 의도, 능력 및 상호 작용을 포함한 에이전트 지식, 인식, 행동 사이의 간극을 좁히기 위해 노력합니다.

On the reasoning side, my research mainly concerns how automated reasoning can be kept computationally tractable, including the use of local search methods. More, you say? Less?

추론 측면에서는 연구는 주로 자동화된 추론을 어떻게 유지할 수 있는지에 관한 것입니다. 로컬 검색 방법의 사용을 포함하여 계산적으로 추적할 수 있습니다. 더라고 하셨나요? 덜?

저서

BIBLIOGRAPHY

헥터 레베스크. 1951. “Hector Levesque #지식표현 #추론분야 #인공지능.” 1951. https://www.cs.toronto.edu/~hector/.

———. 2017. Thinking as Computation: A First Course. MIT Press. https://books.google.com?id=uL34DwAAQBAJ.

———. 2018. Common Sense, the Turing Test, and the Quest for Real Ai. MIT Press. https://books.google.com?id=0Z4LEAAAQBAJ.

———. 2023. The Logic of Knowledge Bases. https://www.collegepublications.co.uk/logic/?00051.

헥터 레베스크, and Ronald J. Brachman. 2022. Machines like Us: Toward Ai with Common Sense. MIT Press. https://books.google.com?id=yLZNEAAAQBAJ.

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