Michael Genesereth | |
---|---|
Born | [ citation needed ] | 15 October 1948
Alma mater | Massachusetts Institute of Technology Harvard University |
Known for | Artificial Intelligence Computational law General game playing |
Awards | AAAI Fellow (1990, Founding). [1] |
Scientific career | |
Fields | Computer science Logic |
Institutions | Stanford University |
Thesis | Automated Consultation for Complex Computer Systems [2] (1978) |
Doctoral advisor | Thomas Cheatham (Harvard) Joel Moses (MIT) |
Doctoral students | Russell Greiner Jock D. Mackinlay Stuart J. Russell Vishal Sikka |
Michael Genesereth (born 1948) is an American logician and computer scientist, who is most known for his work on computational logic and applications of that work in enterprise management, computational law, and general game playing. [3] Genesereth is professor in the Computer Science Department at Stanford University and a professor by courtesy in the Stanford Law School. [4] His 1987 textbook on Logical Foundations of Artificial Intelligence [5] remains one of the key references on symbolic artificial intelligence. [6] He is the author of the influential Game Description Language (GDL) and Knowledge Interchange Format (KIF), the latter of which led to the ISO Common Logic standard. [7]
Genesereth received a B.S. in Physics (1972) from Massachusetts Institute of Technology, and both an M.S. (1974) and Ph.D. (1978) in Applied Mathematics [8] from Harvard University. As a graduate student, he worked on the Macsyma computer algebra system and wrote his dissertation on an automated advisor for Macsyma users. [9]
Genesereth has been a faculty member in the computer science department at Stanford University since 1979. He is the director of the Logic Group at Stanford and a founder and the research director of the Stanford CodeX Center for Legal Informatics. [10] [11] [12] [13] [14] [15] He is one of the founders of the companies Teknowledge, [16] CommerceNet, [17] Mergent Systems, [18] SIPX [19] and Symbium. [20] Symbium is the most recent spinoff from the computational law research undertaken by CodeX and is a winner of the Ivory Innovation Prize for Policy and Regulatory Reform. [21]
Genesereth's research [22] is broadly based on the use of computational logic for such applications as integrating knowledge from heterogeneous sources, [23] as a common format for exchanging knowledge, [24] [25] [26] as a foundation for agent-based knowledge representation and software engineering, [27] [28] [29] [30] as an enhancement to spreadsheets known as a Logical spreadsheet, [31] and for optimizing queries in a deductive database system. [32] [33] He invented the notion of Model-based Diagnosis as a contrast with the symptom-based approach then current in systems like Mycin, and this was recognized by its inclusion in a retrospective on fifty volumes of Artificial Intelligence (journal). [34] His work on data integration [35] won the best paper prize at the 1997 Symposium on Principles of Database Systems. [36] His work on deals among rational agents [37] won the influential paper award by the International Foundation for Autonomous Agents and Multiagent Systems in 2007. [38]
In 2016, Genesereth launched an effort to bring logic education to high schools across America [39] [40] citing Herbrand semantics [41] as the foundation of the pedagogical approach. The program includes summer camps for high school students offered on the Stanford campus, and teacher professional development offered across different studies in the USA. [42] The high school offerings utilize the same course material as a MOOC on the same topic. [43]
Genesereth served as the program chair of the Third National Conference of Artificial Intelligence, [44] councilor of American Association for the Advancement of Artificial Intelligence for the term expiring in 1985, [45] and was elected its fellow in 1990. [1] He has also been an organizer of the International General Game Playing Competition, [46] a program chair in 1993 of The Web Conference, [47] a co-organizer of 2010 American Association for the Advancement of Artificial Intelligence Spring Symposium on Intelligent Privacy Management, [48] and a chair of the Ninth Symposium on Abstraction, Reformulation and Approximation. [49]
Cyc is a long-term artificial intelligence project that aims to assemble a comprehensive ontology and knowledge base that spans the basic concepts and rules about how the world works. Hoping to capture common sense knowledge, Cyc focuses on implicit knowledge that other AI platforms may take for granted. This is contrasted with facts one might find somewhere on the internet or retrieve via a search engine or Wikipedia. Cyc enables semantic reasoners to perform human-like reasoning and be less "brittle" when confronted with novel situations.
Distributed artificial intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems. DAI is closely related to and a predecessor of the field of multi-agent systems.
In artificial intelligence, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic and search. Symbolic AI used tools such as logic programming, production rules, semantic nets and frames, and it developed applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. The Symbolic AI paradigm led to seminal ideas in search, symbolic programming languages, agents, multi-agent systems, the semantic web, and the strengths and limitations of formal knowledge and reasoning systems.
Reason maintenance is a knowledge representation approach to efficient handling of inferred information that is explicitly stored. Reason maintenance distinguishes between base facts, which can be defeated, and derived facts. As such it differs from belief revision which, in its basic form, assumes that all facts are equally important. Reason maintenance was originally developed as a technique for implementing problem solvers. It encompasses a variety of techniques that share a common architecture: two components—a reasoner and a reason maintenance system—communicate with each other via an interface. The reasoner uses the reason maintenance system to record its inferences and justifications of the inferences. The reasoner also informs the reason maintenance system which are the currently valid base facts (assumptions). The reason maintenance system uses the information to compute the truth value of the stored derived facts and to restore consistency if an inconsistency is derived.
Logic in computer science covers the overlap between the field of logic and that of computer science. The topic can essentially be divided into three main areas:
A knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. Knowledge-based systems were the focus of early artificial intelligence researchers in the 1980s. The term can refer to a broad range of systems. However, all knowledge-based systems have two defining components: an attempt to represent knowledge explicitly, called a knowledge base, and a reasoning system that allows them to derive new knowledge, known as an inference engine.
Robert Anthony Kowalski is an American-British logician and computer scientist, whose research is concerned with developing both human-oriented models of computing and computational models of human thinking. He has spent most of his career in the United Kingdom.
In computer science multi-agent planning involves coordinating the resources and activities of multiple agents.
Deborah Louise McGuinness is an American computer scientist and researcher at Rensselaer Polytechnic Institute (RPI). She is a professor of Computer, Cognitive and Web Sciences, Industrial and Systems Engineering, and an endowed chair in the Tetherless World Constellation, a multidisciplinary research institution within RPI that focuses on the study of theories, methods and applications of the World Wide Web. Her fields of expertise include interdisciplinary data integration, artificial intelligence, specifically in knowledge representation and reasoning, description logics, the semantic web, explanation, and trust.
Game Description Language (GDL) is a specialized logic programming language designed by Michael Genesereth. The goal of GDL is to allow the development of AI agents capable of general game playing. It is part of the General Game Playing Project at Stanford University.
Nils John Nilsson was an American computer scientist. He was one of the founding researchers in the discipline of artificial intelligence. He was the first Kumagai Professor of Engineering in computer science at Stanford University from 1991 until his retirement. He is particularly known for his contributions to search, planning, knowledge representation, and robotics.
Moshe Ya'akov Vardi is an Israeli mathematician and computer scientist. He is the Karen Ostrum George Distinguished Service Professor in Computational Engineering at Rice University, United States. and a faculty advisor for the Ken Kennedy Institute. His interests focus on applications of logic to computer science, including database theory, finite model theory, knowledge of multi-agent systems, computer-aided verification and reasoning, and teaching logic across the curriculum. He is an expert in model checking, constraint satisfaction and database theory, common knowledge (logic), and theoretical computer science.
A logical spreadsheet is a spreadsheet in which formulas take the form of logical constraints rather than function definitions.
Eric Joel Horvitz is an American computer scientist, and Technical Fellow at Microsoft, where he serves as the company's first Chief Scientific Officer. He was previously the director of Microsoft Research Labs, including research centers in Redmond, WA, Cambridge, MA, New York, NY, Montreal, Canada, Cambridge, UK, and Bangalore, India.
Yoav Shoham is a computer scientist and a Professor Emeritus at Stanford University. His research spans artificial intelligence, logic and game theory. He has also founded and sold several AI companies.
Kevin Leyton-Brown is a Professor of Computer Science at the University of British Columbia. He received his Ph.D. at Stanford University in 2003. He was the recipient of a 2014 NSERC E.W.R. Steacie Memorial Fellowship, a 2013/14 Killam Teaching Prize, and a 2013 Outstanding Young Computer Science Researcher Prize from the Canadian Association of Computer Science. Leyton-Brown co-teaches a popular game theory course on Coursera.org, along with Matthew O. Jackson and Yoav Shoham. Leyton-Brown serves as an associate editor for the Journal of Artificial Intelligence Research, the Artificial Intelligence journal, and ACM Transactions on Economics and Computation, and was program chair for the ACM Conference on Electronic Commerce in 2012. Leyton-Brown and coauthors have received the IJCAI-JAIR Best Paper Prize and numerous medals in international SAT competitions (2003–12). He was elected an AAAI Fellow in 2018.
Michael John Wooldridge is a professor of computer science at the University of Oxford. His main research interests is in multi-agent systems, and in particular, in the computational theory aspects of rational action in systems composed of multiple self-interested agents. His work is characterised by the use of techniques from computational logic, game theory, and social choice theory.
Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant and others, the effective construction of rich computational cognitive models demands the combination of symbolic reasoning and efficient machine learning. Gary Marcus, argued, "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning." Further, "To build a robust, knowledge-driven approach to AI we must have the machinery of symbol manipulation in our toolkit. Too much useful knowledge is abstract to proceed without tools that represent and manipulate abstraction, and to date, the only known machinery that can manipulate such abstract knowledge reliably is the apparatus of symbol manipulation."
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