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Sökning: WFRF:(Kotthoff Lars)

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1.
  • Agrawal, Vikas, et al. (författare)
  • The AAAI-13 Conference Workshops
  • 2013
  • Ingår i: The AI Magazine. - : Association for the Advancement of Artificial Intelligence. - 0738-4602 .- 2371-9621. ; 34:4, s. 108-115
  • Tidskriftsartikel (refereegranskat)abstract
    • The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14-15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity,, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16)
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2.
  • Bessiere, Christian, et al. (författare)
  • The Inductive Constraint Programming Loop
  • 2017
  • Ingår i: IEEE Intelligent Systems. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 1541-1672 .- 1941-1294. ; 32:5, s. 44-52
  • Tidskriftsartikel (refereegranskat)abstract
    • Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.
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3.
  • Bessiere, Christian, et al. (författare)
  • The Inductive Constraint Programming Loop
  • 2016
  • Ingår i: Data Mining and Constraint Programming. - Cham : Springer International Publishing. - 9783319501369 - 9783319501376 ; , s. 303-309
  • Bokkapitel (refereegranskat)abstract
    • Constraint programming is used for a variety of real-world optimisa-tion problems, such as planning, scheduling and resource allocation prob-lems. At the same time, one continuously gathers vast amounts of dataabout these problems. Current constraint programming software does notexploit such data to update schedules, resources and plans. We propose anew framework, that we call theInductive Constraint Programming loop.In this approach data is gathered and analyzed systematically, in order todynamically revise and adapt constraints and optimization criteria. In-ductive Constraint Programming aims at bridging the gap between theareas of data mining and machine learning on the one hand, and constraintprogramming on the other hand.
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  • Resultat 1-4 av 4

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