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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.
Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. The term "inductive" here refers to philosophical rather than mathematical induction. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance. While machine learning algorithms have shown remarkable performances on various tasks, they are susceptible to inheriting and amplifying biases present in their training data. This can manifest in skewed representations or unfair treatment of different demographics, such as those based on race, gender, language, and cultural groups.
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.
Ray Solomonoff was the inventor of algorithmic probability, his General Theory of Inductive Inference, and was a founder of algorithmic information theory. He was an originator of the branch of artificial intelligence based on machine learning, prediction and probability. He circulated the first report on non-semantic machine learning in 1956.
In artificial intelligence, an intelligent agent (IA) is an agent acting in an intelligent manner; It perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An intelligent agent may be simple or complex: A thermostat or other control system is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state, or a biome.
Probabilistic logic involves the use of probability and logic to deal with uncertain situations. Probabilistic logic extends traditional logic truth tables with probabilistic expressions. A difficulty of probabilistic logics is their tendency to multiply the computational complexities of their probabilistic and logical components. Other difficulties include the possibility of counter-intuitive results, such as in case of belief fusion in Dempster–Shafer theory. Source trust and epistemic uncertainty about the probabilities they provide, such as defined in subjective logic, are additional elements to consider. The need to deal with a broad variety of contexts and issues has led to many different proposals.
Keith Leonard Clark is an Emeritus Professor in the Department of Computing at Imperial College London, England.
The following outline is provided as an overview of and topical guide to artificial intelligence:
Stephen H. Muggleton FBCS, FIET, FAAAI, FECCAI, FSB, FREng is Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial College London.
Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use first-order logic to describe relational properties of a domain in a general manner and draw upon probabilistic graphical models to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s.
The Turing Institute was an artificial intelligence laboratory in Glasgow, Scotland, between 1983 and 1994. The company undertook basic and applied research, working directly with large companies across Europe, the United States and Japan developing software as well as providing training, consultancy and information services.
Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative and often recursive programs from incomplete specifications, such as input/output examples or constraints.
Action model learning is an area of machine learning concerned with creation and modification of software agent's knowledge about effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in logic-based action description language and used as the input for automated planners.
This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence, its sub-disciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.
PRAC (Probabilistic Action Cores) is an interpreter for natural-language instructions for robotic applications developed at the Institute for Artificial Intelligence at the University of Bremen, Germany, and is supported in parts by the European Commission and the German Research Foundation (DFG).
The following outline is provided as an overview of and topical guide to machine learning:
Kristian Kersting is a German computer scientist. He is Professor of Artificial intelligence and Machine Learning at the Department of Computer Science at the Technische Universität Darmstadt, Head of the Artificial Intelligence and Machine Learning Lab (AIML) and Co-Director of hessian.AI, the Hessian Center for Artificial Intelligence.
Jens Lehmann is a computer scientist, who works with knowledge graphs and artificial intelligence. He is a principal scientist at Amazon, an honorary professor at TU Dresden and a fellow of European Laboratory for Learning and Intelligent Systems. Formerly, he was a full professor at the University of Bonn, Germany and lead scientist for Conversational AI and Knowledge Graphs at Fraunhofer IAIS.
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 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."
References
- ↑ "Home - Professor Alessandra Russo". imperial.ac.uk. Retrieved 11 May 2023.
- 1 2 "Alessandra Russo - CV" (PDF). Retrieved 11 May 2023.
- ↑ "SPIKE - Structured and Probabilistic Intelligent Knowledge Engineering" . Retrieved 11 May 2023.
- ↑ "Awards & Invited Talks - Professor Alessandra Russo" . Retrieved 11 May 2023.
- ↑ "Alessandra Russo - Safe & Trusted AI" . Retrieved 11 May 2023.
- ↑ Law, Mark; Russo, Alessandra; Broda, Krysia (2014). "Inductive Learning of Answer Set Programs". Proceedings of the Fourteenth European Conference on Logics in Artificial Intelligence, 2014, Funchal, Madeira, Portugal, September 2426, 2014. Fourteenth European Conference on Logics in Artificial Intelligence. Springer, Berlin, Heidelberg. doi:10.1007/978-3-319-11558-0_22. hdl: 10044/1/23794 .
- ↑ Law, Mark; Russo, Alessandra; Broda, Krysia (2016). "Iterative Learning of Answer Set Programs from Context Dependent Examples". Theory and Practice of Logic Programming, Volume 16, Special Issue 5-6: 32nd International Conference on Logic Programming. 32nd International Conference on Logic Programming. Cambridge University Press. arXiv: 1608.01946 . doi:10.1017/S1471068416000351.
