Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. Such machines may be called AIs.
In the field of artificial intelligence (AI), tasks that are hypothesized to require artificial general intelligence to solve are informally known as AI-complete or AI-hard. Calling a problem AI-complete reflects the belief that it cannot be solved by a simple specific algorithm.
A multi-agent system is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning.
A blackboard system is an artificial intelligence approach based on the blackboard architectural model, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts.
Automated planning and scheduling, sometimes denoted as simply AI planning, is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles. Unlike classical control and classification problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to decision theory.
The Planning Domain Definition Language (PDDL) is an attempt to standardize Artificial Intelligence (AI) planning languages. It was first developed by Drew McDermott and his colleagues in 1998 mainly to make the 1998/2000 International Planning Competition (IPC) possible, and then evolved with each competition. The standardization provided by PDDL has the benefit of making research more reusable and easily comparable, though at the cost of some expressive power, compared to domain-specific systems.
In computer science multi-agent planning involves coordinating the resources and activities of multiple agents.
The blocks world is a planning domain in artificial intelligence. The algorithm is similar to a set of wooden blocks of various shapes and colors sitting on a table. The goal is to build one or more vertical stacks of blocks. Only one block may be moved at a time: it may either be placed on the table or placed atop another block. Because of this, any blocks that are, at a given time, under another block cannot be moved. Moreover, some kinds of blocks cannot have other blocks stacked on top of them.
In artificial intelligence, hierarchical task network (HTN) planning is an approach to automated planning in which the dependency among actions can be given in the form of hierarchically structured networks.
Drew McDermott was a professor of Computer Science at Yale University. He was known for his contributions in artificial intelligence and automated planning.
There are a number of competitions and prizes to promote research in artificial intelligence.
Sven Koenig is a full professor in computer science at the University of Southern California. He received an M.S. degree in computer science from the University of California at Berkeley in 1991 and a Ph.D. in computer science from Carnegie Mellon University in 1997, advised by Reid Simmons.
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.
Karen L. Myers is the director of SRI International's Artificial Intelligence Center, where she is also principal scientist.
Sheila McIlraith is a Canadian computer scientist specializing in Artificial Intelligence (AI). She is a Professor in the Department of Computer Science, University of Toronto. She is a Canada CIFAR AI Chair, a faculty member of the Vector Institute, and Associate Director and Research Lead of the Schwartz Reisman Institute for Technology and Society.
Thomas L. Dean is an American computer scientist known for his work in robot planning, probabilistic graphical models, and computational neuroscience. He was one of the first to introduce ideas from operations research and control theory to artificial intelligence. In particular, he introduced the idea of the anytime algorithm and was the first to apply the factored Markov decision process to robotics. He has authored several influential textbooks on artificial intelligence.
Adele E. Howe was an American computer scientist specializing in artificial intelligence. She was one of the developers of the Planning Domain Definition Language for automated planning and scheduling, and was also known for her work on metasearch engines. She was a Professor Laureate in the College of Natural Sciences at Colorado State University.
Hector Geffner is an Argentinian computer scientist and a Alexander von Humboldt Professor of artificial intelligence at RWTH Aachen University and Wallenberg Guest Professor in AI at Linköping University. His research interests are focused on artificial intelligence, especially automated planning and the integration of model-based AI and data-based AI. He is best known for his work on domain-independent heuristic planning and received several International Conference on Automated Planning and Scheduling (ICAPS) influential paper awards. Previously he held a research professorship at ICREA and the Artificial Intelligence and Machine Learning Group at University Pompeu Fabra in Barcelona since 2001. He was a staff researcher at the IBM Thomas J. Watson Research Center from 1990 to 1992 and a professor at Simón Bolívar University in Caracas, Venezuela from 1992 to 2001. Geffner was awarded an ERC Advanced Grant in 2020 to explore the connection between machine learning and model-based AI, and is a former board member and current fellow of the European Association for Artificial Intelligence (EurAI).. He was elected an AAAI Fellow in 2007.
Michela Milano is an Italian computer scientist whose research in artificial intelligence includes work on constraint programming, multi-agent systems, metaheuristics, decision support, high-performance computing, and green computing. She is a professor in the Department of Computer Science and Engineering at the University of Bologna, where she heads the Centro Interdipartimentale Alma Mater Research Institute for Human-Centered Artificial Intelligence. She is the former editor-in-chief of the journal Constraints.
Brighton, United Kingdom
Sankt Augustin, Germany
College Park, MD , United States
Vadstena, Sweden
Assisi, Italy
Toulouse, France
Toledo, Spain
Whistler, Canada
Sydney, Australia
Thessaloniki, Greece
Atibaia, São Paulo, Brazil
Jerusalem, Israel
Delft, The Netherlands
Guangzhou, China – Virtual Conference
Singapore – Virtual Conference
Prague, Czech Republic