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Sara Bernardini
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Michael Cashmore
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Chiara Piacentini
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Planning Research at King's is focussed around domain-independent planning. Planning is an active research area within the broader field of Artificial Intelligence, and has strong links with scheduling and optimisation research. The primary problem is the identification of sequences of actions which will achieve specified goals from specified initial conditions. Domain-independent planning, which is concerned with the fundamental principles of planning as an activity, typically concentrating on search techniques, search control techniques and the representation and treatment, within a planning problem, of uncertainty, non-determinism, resources, time, functional interdependence and other issues. Planning has been successfully applied to a range of fielded industrial applications and we actively seek new industrial problems to which planning could be applied.
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ICAPS 2013
Tuesday, 29 January 2013 09:39
Andrew Coles
 The group have had a number of papers accepted for the forthcoming ICAPS 2013 conference:
- "Partially Grounded Planning as Quantified Boolean Formula", Michael Cashmore, Maria Fox and Enrico Giunchiglia
- "Searching for Good Solutions in Goal-Dense Search Spaces", Andrew Coles and Amanda Coles
- "Planning-based Social Partners for Children with Autism", Sara Bernardini
- "Autonomous Search and Tracking via Temporal Planning", Sara Bernardini, Maria Fox, Derek Long and John Bookless
- "Combining a Temporal Planner with an External Solver for the Power Balancing Problem in an Electricity Network", Chiara Piacentini, Varvara D. Alimisis, Maria Fox and Derek Long
- "Challenge: Modelling Unit Commitment as a Planning Problem", Josh Campion, Chris Dent, Maria Fox, Derek Long and Daniele Magazzeni
Last Updated on Monday, 11 March 2013 12:13
JAIR Article on Policy Learning
Wednesday, 04 July 2012 13:15
Dan
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
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JAIR Article on COLIN
Friday, 25 May 2012 16:12
Andrew Coles
A paper on COLIN, our flagship planner for problems with Continuous Linear Numeric Effects, has been published in Volu me 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
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L-RPG: Introducing a Lifted Forward-Chaining Planner
Monday, 23 July 2012 12:24
Bram Ridder
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
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OPTIC: Optimising Preferences and Time-Dependent Costs
Thursday, 28 June 2012 13:47
Andrew Coles
 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
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ECAI 2012
Wednesday, 23 May 2012 15:11
Andrew Coles
 The 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
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