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AIPS
2007

174views Artificial Intelligence» more AIPS 2007»

Learning to Plan Using Harmonic Analysis of Diffusion Models

13 years 6 months ago

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This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...

Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...

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Id	Views	Title	Status

1	174	Learning to Plan Using Harmonic Analysis of Diffusion Models	claim
2	171	A Fast Incremental Algorithm for Maintaining Dispatchability of Partially Controllable Plans	claim
3	159	Act Local, Think Global: Width Notions for Tractable Planning	claim
4	156	Approximate Solution Techniques for Factored First-Order MDPs	claim
5	151	itSIMPLE 2.0: An Integrated Tool for Designing Planning Domains	claim
6	150	Mixed Integer Linear Programming for Exact Finite-Horizon Planning in Decentralized Pomdps	claim
7	147	Angelic Semantics for High-Level Actions	claim
8	146	Online Planning for Resource Production in Real-Time Strategy Games	claim
9	135	Context-Aware Logistic Routing and Scheduling	claim
10	123	Robust Local Search and Its Application to Generating Robust Schedules	claim
11	120	A Hybrid Linear Programming and Relaxed Plan Heuristic for Partial Satisfaction Planning Problems	claim
12	119	Concurrent Probabilistic Temporal Planning with Policy-Gradients	claim
13	118	FF-Replan: A Baseline for Probabilistic Planning	claim
14	115	Bounding the Resource Availability of Activities with Linear Resource Impact	claim
15	112	Cost-Sharing Approximations for h+	claim
16	107	Transformational Planning for Everyday Activity	claim
17	104	Discovering Relational Domain Features for Probabilistic Planning	claim
18	104	Planning with Diversified Models for Fault-Tolerant Robots	claim
19	102	Flexible Abstraction Heuristics for Optimal Sequential Planning	claim
20	100	Evaluating Temporal Planning Domains	claim
21	100	The Manpower Allocation Problem with Time Windows and Job-Teaming Constraints	claim
22	98	Managing Personal Tasks with Time Constraints and Preferences	claim
23	98	Learning Macro-Actions for Arbitrary Planners and Domains	claim
24	97	Planning with Respect to an Existing Schedule of Events	claim
25	96	A New Local-Search Algorithm for Forward-Chaining Planning	claim
26	95	Towards a Heuristic for Scheduling the James Webb Space Telescope	claim
27	92	Dynamic Control in Path-Planning with Real-Time Heuristic Search	claim
28	92	An Innovative Product for Space Mission Planning: An A Posteriori Evaluation	claim
29	87	Temporally-Expressive Planning as Constraint Satisfaction Problems	claim
30	87	Using Adaptive Priority Weighting to Direct Search in Probabilistic Scheduling	claim
31	86	Planning Robust Temporal Plans: A Comparison Between CBTP and TGA Approaches	claim
32	83	Monitoring Plan Optimality During Execution	claim
33	81	Structural Patterns of Tractable Sequentially-Optimal Planning	claim
34	81	Gradient-Based Relational Reinforcement Learning of Temporally Extended Policies	claim
35	80	Prioritizing Bellman Backups without a Priority Queue	claim
36	76	FF + FPG: Guiding a Policy-Gradient Planner	claim
37	72	Generating Exponentially Smaller POMDP Models Using Conditionally Irrelevant Variable Abstraction	claim
38	68	Online Identification of Useful Macro-Actions for Planning	claim
39	63	On the Hardness of Planning Problems with Simple Causal Graphs	claim

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