AIPS 2003 - PDF Preprint Papers

AIPS 2003

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AIPS
2003

179views Artificial Intelligence» more AIPS 2003»

A Multi-Agent System-driven AI Planning Approach to Biological Pathway Discovery

13 years 6 months ago

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As genomic and proteomic data is collected from highthroughput methods on a daily basis, subcellular components are identiﬁed and their in vitro behavior is characterized. Howev...

Salim Khan, William Gillis, Carl Schmidt, Keith De...

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Id	Views	Title	Status

1	179	A Multi-Agent System-driven AI Planning Approach to Biological Pathway Discovery	claim
2	149	Synthesis of Hierarchical Finite-State Controllers for POMDPs	claim
3	140	Vehicle Routing and Job Shop Scheduling: What's the Difference?	claim
4	131	A Framework for Planning in Continuous-time Stochastic Domains	claim
5	130	Exploiting a Graphplan Framework in Temporal Planning	claim
6	119	Guided Symbolic Universal Planning	claim
7	114	A Multi-Heuristic GA for Schedule Repair in Precast Plant Production	claim
8	114	Local Search Techniques for Temporal Planning in LPG	claim
9	114	Optimal Rectangle Packing: Initial Results	claim
10	107	GIPO II: HTN Planning in a Tool-supported Knowledge Engineering Environment	claim
11	105	Quality and Utility - Towards a Generalization of Deadline and Anytime Scheduling	claim
12	104	Reasoning about Autonomous Processes in an Estimated-Regression Planner	claim
13	100	Economically Augmented Job Shop Scheduling	claim
14	99	Branching Matters: Alternative Branching in Graphplan	claim
15	99	Decision-Theoretic Group Elevator Scheduling	claim
16	98	GPT Meets PSR	claim
17	97	A Mixed-initiative Framework for Robust Plan Sketching	claim
18	96	Automated Planning and Scheduling using Calculus of Variations in Discrete Space	claim
19	94	Symmetry Reduction for SAT Representations of Transition Systems	claim
20	93	A Framework for Planning with Extended Goals under Partial Observability	claim
21	93	Labeled RTDP: Improving the Convergence of Real-Time Dynamic Programming	claim
22	92	The Role of Planning in Grid Computing	claim
23	90	Recommendation as a Stochastic Sequential Decision Problem	claim
24	90	Conformant Probabilistic Planning via CSPs	claim
25	89	Learning Rules for Adaptive Planning	claim
26	88	Expressive Equivalence of Formalisms for Planning with Sensing	claim
27	81	Localizing Planning with Functional Process Models	claim
28	80	Planning for Marketing Campaigns	claim
29	77	SOFIA's Choice: Scheduling Observations for an Airborne Observatory	claim

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