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In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems, assuming intelligence is computational, is equivalent to that of solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI. To call a problem AI-complete reflects an attitude that it would not be solved by a simple specific algorithm.
Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.
Planner is a programming language designed by Carl Hewitt at MIT, and first published in 1969. First, subsets such as Micro-Planner and Pico-Planner were implemented, and then essentially the whole language was implemented as Popler by Julian Davies at the University of Edinburgh in the POP-2 programming language. Derivations such as QA4, Conniver, QLISP and Ether were important tools in artificial intelligence research in the 1970s, which influenced commercial developments such as Knowledge Engineering Environment (KEE) and Automated Reasoning Tool (ART).
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.
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:
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, in particular in knowledge representation and reasoning and metalogic, the area of automated reasoning is dedicated to understanding different aspects of reasoning. The study of automated reasoning helps produce computer programs that allow computers to reason completely, or nearly completely, automatically. Although automated reasoning is considered a sub-field of artificial intelligence, it also has connections with theoretical computer science and philosophy.
Alan Richard Bundy is a professor at the School of Informatics at the University of Edinburgh, known for his contributions to automated reasoning, especially to proof planning, the use of meta-level reasoning to guide proof search.
The following outline is provided as an overview of and topical guide to artificial intelligence:
Drew McDermott was a professor of Computer Science at Yale University. He was known for his contributions in artificial intelligence and planning.
Leonhard Wolfgang Bibel is a German computer scientist, mathematician and Professor emeritus at the Department of Computer Science of the Technische Universität Darmstadt. He was one of the founders of the research area of artificial intelligence in Germany and Europe and has been named as one of the ten most important researchers in the German artificial intelligence history by the Gesellschaft für Informatik. Bibel established the necessary institutions, conferences and scientific journals and promoted the necessary research programs to establish the field of artificial intelligence.
Carl Eddie Hewitt was an American computer scientist who designed the Planner programming language for automated planning and the actor model of concurrent computation, which have been influential in the development of logic, functional and object-oriented programming. Planner was the first programming language based on procedural plans invoked using pattern-directed invocation from assertions and goals. The actor model influenced the development of the Scheme programming language, the π-calculus, and served as an inspiration for several other programming languages.
Holger H. Hoos is a German-Canadian computer scientist and a Alexander von Humboldt-professor of artificial intelligence at RWTH Aachen University. He also holds a part-time appointment as a professor of machine learning at Leiden University, and he is an adjunct professor at the Computer Science Department of the University of British Columbia, where he held a full-time professorial appointment from 2000 until 2016. His research interests are focused on artificial intelligence, at the intersection of machine learning, automated reasoning and optimization, with applications in empirical algorithmics, bioinformatics and operations research. In particular, he works on automated algorithm design and on stochastic local search algorithms. Since 2015, he is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and since 2020 a Fellow of the European Association for Artificial Intelligence (EurAI) as well as a Fellow of the Association for Computing Machinery (ACM).
GOFAI is an acronym for "Good Old-Fashioned Artificial Intelligence" invented by the philosopher John Haugeland in his 1985 book, Artificial Intelligence: The Very Idea. Technically, GOFAI refers only to a restricted kind of symbolic AI, namely rule-based or logical agents. This approach was popular in the 1980s, especially as an approach to implementing expert systems, but symbolic AI has since been extended in many ways to better handle uncertain reasoning and more open-ended systems. Some of these extensions include probabilistic reasoning, non-monotonic reasoning, multi-agent systems, and neuro-symbolic systems. Significant contributions of symbolic AI, not encompassed by the GOFAI view, include search algorithms; automated planning and scheduling; constraint-based reasoning; the semantic web; ontologies; knowledge graphs; non-monotonic logic; circumscription; automated theorem proving; and symbolic mathematics. For a more complete list, see the main article on symbolic AI.
Francesca Rossi is an Italian computer scientist, currently working at the IBM T.J. Watson Research Lab as an IBM Fellow and the IBM AI Ethics Global Leader.
Sheila Ann McIlraith is a Canadian computer scientist whose research topics include artificial intelligence and the Semantic Web. She is a professor of computer science at the University of Toronto.
Diego Calvanese is an Italian Computer Scientist and Professor at the Faculty of Computer Science at the Free University of Bozen-Bolzano. In addition, since 2019, he is Wallenberg visiting Professor at the Department of Computing Science, Umeå University. He is well known for his scientific contributions in knowledge representation and reasoning in AI, description logics, and database theory.
Nicola Leone is an Italian computer scientist who works in the areas of artificial intelligence, knowledge representation and reasoning, and database theory. Leone is currently the rector of the University of Calabria and a professor of Computer Science. Previously, he was a professor of Database Systems at the TU Wien.
Neuro-symbolic AI integrates neural and symbolic AI architectures to address complementary strengths and weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Valiant and many others, the effective construction of rich computational cognitive models demands the combination of sound symbolic reasoning and efficient machine learning models. Gary Marcus, argues that: "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 of useful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we know of that can manipulate such abstract knowledge reliably is the apparatus of symbol-manipulation."
Sylvie Thiébaux is a French-Australian computer scientist, whose research in artificial intelligence focuses on automated planning and scheduling, diagnosis, and automated reasoning under uncertainty. She is a professor of computer science at the Australian National University, and co-editor-in-chief of the journal Artificial Intelligence.
References
- ↑ Birth year from Library of Congress catalog entry, retrieved 2022-06-20
- 1 2 3 4 5 6 7 8 "Luigia Carlucci Aiello: Biography", Department of Computer, Control and Management Engineering, Sapienza University of Rome, retrieved 2022-06-20
- 1 2 3 4 5 6 7 Stock, Oliviero; Schaerf, Marco, eds. (2006), Reasoning, Action and Interaction in AI Theories and Systems: Essays Dedicated to Luigia Carlucci Aiello, Lecture Notes in Computer Science, vol. 4155, Springer, doi:10.1007/11829263, ISBN 978-3-540-37901-0 ; see in particular Foreword, pp. v–xi
- ↑ Elected fellows, Association for the Advancement of Artificial Intelligence, retrieved 2022-06-20
- 1 2 "Luigia Carlucci Aiello", InspiringFifty: Italy, retrieved 2022-06-20
