Planning at King's

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Planning at King's

L-RPG: Introducing a Lifted Forward-Chaining Planner

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L-RPG is a domain-independent Forward-Chaining planner which - unlike many other planners - does not rely on grounding for guiding its search through the search space. The ultimate aim of this planner is to be more scalable than planners which rely on grounding and solve bigger problem instances which cannot currently be solved by state-of-the-art planners due to memory constraints.


Last Updated on Tuesday, 24 July 2012 10:42

JAIR Article on Policy Learning

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A paper on plan-based policy-learning has been published in Volume 44 of the Journal of Artificial Intelligence Research:

"Plan-based Policies for Efficient Multiple Battery Load Management" M. Fox,  D. Long and D. Magazzeni Journal of Artificial Intelligence Research. 44. 2012. pp. 335–382

Click 'Read More' for the abstract.



Last Updated on Wednesday, 04 July 2012 13:17

JAIR Article on COLIN

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A paper on COLIN, our flagship planner for problems with Continuous Linear Numeric Effects, has been published in Volume 44 of the Journal of Artificial Intelligence Research:

"COLIN: Planning with Continuous Linear Numeric Change" A. J. Coles, A. I. Coles, M. Fox and D. Long Journal of Artificial Intelligence Research. 44. 2012. pp. 1–96

Click 'Read More' for the abstract.


Last Updated on Friday, 25 May 2012 15:20

Bridging the Gap

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Two pilot studies involving AI planning have been funded through the Interdisciplinary Informatics - Bridging the Gaps programme, working with researchers in Neuroimaging and Robotics.  The projects explore the use of planning in novel application areas, continuing the group's recent work on planning for problems with time and numeric resources.

Last Updated on Friday, 27 April 2012 15:22

OPTIC: Optimising Preferences and Time-Dependent Costs

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OPTIC is a temporal planner for use in problems where plan cost is determined by preferences or time-dependent goal-collection costs.  Such problems arise in a range of interesting situations, from scheduling the delivery of perishable goods, to coordinating order-fulfillment activities in warehouses.

Last Updated on Friday, 07 December 2012 13:40

ECAI 2012

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ECAI 2012The group has had two conference papers accepted at ECAI 2012:

  • Opportunistic Branched Plans to Maximise Utility in the Presence of Resource Uncertainty. A. J. Coles.
  • Planning as Quantified Boolean Formula. M. Cashmore, M. Fox and E. Giunchiglia.
Last Updated on Friday, 25 May 2012 15:14

Plan-Based Policy-Learning for Surveillance Problems (M. Fox, D. Long, A. Coles, and S. Bernardini)

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An RQ-7B UAB.  Image public domain, from group has successfully secured ESPRC funds (EP/J012157/1, fEC value £458,818) to research the use of planning to underpin policy learning for surveillance problems.  Surveillance problems give rise to many challenges including the management of uncertainty in an unpredictable environment, the management of restricted resources and the communication of commitments and requests between multiple heterogeneous agent 'observers'. At the heart of surveillance problems lies the need to plan complex sequences of behaviour that achieve surveillance goals. These goals are typically expressed in terms of gathering as much information as possible given constraints, and communicating findings to a human operator.

Last Updated on Monday, 15 July 2013 14:41
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