New Approaches to Design and Control of Time Limited Search Algorithms

  • Partha Pratim Chakrabarti
  • Sandip Aine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


We talk about two key aspects of the quality-time trade-offs in time limited search based reasoning namely, design of efficient anytime algorithms and formulations for meta-reasoning (or control) to optimize the computational trade-off under various constrained environments. We present the ideas behind novel anytime heuristic search algorithms, both contract and interruptible. We also describe new meta-control strategies that address parameter control along with time deliberation.


Problem Instance Time Allocation Beam Search Heuristic Search Algorithm Contract Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Boddy, M., Dean, T.: Deliberation scheduling for problem solving in time-constrained environments. Artificial Intelligence 67, 245–285 (1994)zbMATHCrossRefGoogle Scholar
  2. 2.
    Aine, S.: New Approaches to Design and Control of Anytime Algorithmd. PhD thesis, Indian Institute of Technology Kharagpur, Department of Computer Science and Engineering, IIT Kharagpur 721302 India (2008)Google Scholar
  3. 3.
    van Laarhoven, P., Aarts, E.: Simulated Annealing: Theory and Applications. Kluwer, Dordrecht (1992)Google Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  5. 5.
    Pohl, I.: Heuristic search viewed as path finding in a graph. Artif. Intell. 1(3), 193–204 (1970)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Likhachev, M., Gordon, G.J., Thrun, S.: Ara*: Anytime A* with provable bounds on sub-optimality. In: Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge (2004)Google Scholar
  7. 7.
    Zhang, W.: Complete anytime beam search. In: Proceedings of 14th National Conference of Artificial Intelligence AAAI 1998, pp. 425–430. AAAI Press, Menlo Park (1998)Google Scholar
  8. 8.
    Zhou, R., Hansen, E.A.: Beam-stack search: Integrating backtracking with beam search. In: Proceedings of the 15th International Conference on Automated Planning and Scheduling (ICAPS 2005), Monterey, CA, pp. 90–98 (2005)Google Scholar
  9. 9.
    Pearl, J.: Heuristics: intelligent search strategies for computer problem solving. Addison-Wesley Longman Publishing Co., Inc., Boston (1984)Google Scholar
  10. 10.
    Dean, T., Boddy, M.: An analysis of time-dependent planning. In: Proceedings of 6th National Conference on Artificial Intelligence (AAAI 1988), St. Paul, MN, pp. 49–54. AAAI Press, Menlo Park (1988)Google Scholar
  11. 11.
    Hansen, E.A., Zilberstein, S.: Monitoring and control of anytime algorithms: A dynamic programming approach. Artificial Intelligence 126(1-2), 139–157 (2001)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Partha Pratim Chakrabarti
    • 1
  • Sandip Aine
    • 2
  1. 1.Dept of CSEIndian Institute of Technology KharagpurIndia
  2. 2.Mentor Graphics (India) Pvt. Ltd.NoidaIndia

Personalised recommendations