107,431 research outputs found
Learning Domain-Independent Planning Heuristics with Hypergraph Networks
We present the first approach capable of learning domain-independent planning
heuristics entirely from scratch. The heuristics we learn map the hypergraph
representation of the delete-relaxation of the planning problem at hand, to a
cost estimate that approximates that of the least-cost path from the current
state to the goal through the hypergraph. We generalise Graph Networks to
obtain a new framework for learning over hypergraphs, which we specialise to
learn planning heuristics by training over state/value pairs obtained from
optimal cost plans. Our experiments show that the resulting architecture,
STRIPS-HGNs, is capable of learning heuristics that are competitive with
existing delete-relaxation heuristics including LM-cut. We show that the
heuristics we learn are able to generalise across different problems and
domains, including to domains that were not seen during training
Building Organisational Capability: Your Future, Your Business
Much has been written about the benefits to be derived from maximising organisational capability as a means of increasing competitive advantage, establishing human resource functions as a strategic partner and improving stakeholder satisfaction. However, there is very little in the research on how organisations build their organisational capability. This paper proposes a Model of Organisational Capability based on three domains â the Strategic Intent, Organisational Structures and Individual Knowledge. The Model explores how systems and processes can be aligned to maximize organisational capability. The model can be used by researchers to examine the forces that build organisational capability in organisations, and determine critical success factors. Practitioners wishing to maximize their organisational capability can draw on the Model and suggested steps, to assist them to explore the organisational capability agenda for their busines
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
State-of-the-art approaches to partially observable planning like POMCP are
based on stochastic tree search. While these approaches are computationally
efficient, they may still construct search trees of considerable size, which
could limit the performance due to restricted memory resources. In this paper,
we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory
bounded approach to open-loop planning in large POMDPs, which optimizes a fixed
size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four
large benchmark problems and compare its performance with different tree-based
approaches. We show that POSTS achieves competitive performance compared to
tree-based open-loop planning and offers a performance-memory tradeoff, making
it suitable for partially observable planning with highly restricted
computational and memory resources.Comment: Presented at AAAI 201
An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner outperforms all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners' performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a planner can be improved by exploiting additional knowledge, extracted in the form of macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings--planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans
Planning in probabilistic domains using a deterministic numeric planner
In the probabilistic track of the IPC5 - the last International planning competitions - a probabilistic planner based on combining deterministic planning with replanning - FF-REPLAN - out performed the other competitors. This probabilistic planning paradigm discarded the probabilistic information of the domain, just considering for each action its nominal effect as a deterministic effect
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