Listing 1 - 9 of 9 |
Sort by
|
Choose an application
681.3*I2 --- 681.3*I2 Artificial intelligence. AI --- Artificial intelligence. AI
Choose an application
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision,
Choose an application
Choose an application
Constraint programming (Computer science) --- Computer Science --- Engineering & Applied Sciences --- Computer science. --- Computer programming. --- Programming languages (Electronic computers). --- Computer logic. --- Mathematical logic. --- Artificial intelligence. --- Computer Science. --- Programming Languages, Compilers, Interpreters. --- Artificial Intelligence (incl. Robotics). --- Programming Techniques. --- Logics and Meanings of Programs. --- Mathematical Logic and Formal Languages. --- Logic design. --- Artificial Intelligence. --- Design, Logic --- Design of logic systems --- Digital electronics --- Electronic circuit design --- Logic circuits --- Machine theory --- Switching theory --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Informatics --- Science --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Mathematics --- Algebra, Abstract --- Metamathematics --- Set theory --- Syllogism --- Computer science logic --- Logic, Symbolic and mathematical --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Computer languages --- Computer program languages --- Computer programming languages --- Machine language --- Languages, Artificial --- Programming --- Constraint programming (Computer science) - Congresses
Choose an application
Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl's work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.
Choose an application
681.3*I23 <063> --- 681.3*I24 <063> --- 681.3*I26 <063> --- 681.3*I27 <063> --- 681.3*D16 <063> --- 681.3*I2m --- Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence)--Congressen --- Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence)--Congressen --- Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32}--Congressen --- Natural language processing: language generation; language models; language parsing and understanding; machine translation; speech recognition and under-standing; text analysis (Artificial intelligence)--Congressen --- Programming techniques: Logic programming--Congressen --- Artificial intelligence. AI --- Information Technology --- Computer Science (Hardware & Networks) --- 681.3*I2m Artificial intelligence. AI --- 681.3*I26 <063> Learning: analogies; concept learning; induction; knowledge acquisition; language acquisition; parameter learning (Artificial intelligence)--See also {681.3*K32}--Congressen --- 681.3*I24 <063> Knowledge representation formalisms and methods: frames and scripts; predicate logic; relation systems; representation languages; procedural and rule-based representations; semantic networks (Artificial intelligence)--Congressen --- 681.3*I23 <063> Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence)--Congressen
Choose an application
Choose an application
Choose an application
Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning. In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms. Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics.
Listing 1 - 9 of 9 |
Sort by
|