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.
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.
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.
Scalable Urban Traffic Control (SURTRAC) is an adaptive traffic control system developed by researchers at the Robotics Institute, Carnegie Mellon University. SURTAC dynamically optimizes the control of traffic signals to improve traffic flow for both urban grids and corridors; optimization goals include less waiting, reduced traffic congestion, shorter trips, and less pollution. The core control engine combines schedule-driven intersection control with decentralized coordination mechanisms. Since June 2012, a pilot implementation of the SURTRAC system has been deployed on nine intersections in the East Liberty neighborhood of Pittsburgh, Pennsylvania. SURTRAC reduced travel times by more than 25% on average, and wait times were reduced by an average of 40%. A second phase of the pilot program for the Bakery Square district has been running since October 2013. In 2015, Rapid Flow Technologies was formed to commercialize the SURTRAC technology. The lead inventor of this technology, Dr. Xiao-Feng Xie, states that he has no association with and does not provide technical support for this company.
The 2024 Summer Paralympics, also known as the Paris 2024 Paralympic Games, and commonly known as Paris 2024, is an upcoming international multi-sport parasports event governed by the International Paralympic Committee, to be held in Paris, France, from 28 August to 8 September 2024. These games mark the first time Paris will host the Paralympics in its history and the second time that France will host the Paralympic Games, as Tignes and Albertville joint hosted the 1992 Winter Paralympics. The final decision was made by the IOC on 13 September 2017, at their annual session in Lima, Peru.
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.
The New Year's Six, sometimes abbreviated as NY6, are the following NCAA Division I Football Bowl Subdivision (FBS) bowl games: the Rose Bowl, Sugar Bowl, Orange Bowl, Cotton Bowl, Peach Bowl, and Fiesta Bowl. These games are played annually on or around New Year's Day and represent six of the ten oldest bowl games played at the FBS level.
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.
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