FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning
Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search efficiency. We propose here a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling language without loosing efficiency. FAPE's representation coherently integrates flexible timelines with hierarchical refinement methods that can provide efficient control knowledge. A novel reachability analysis technique is proposed and used to develop causal networks to constrain the search space. It is employed for the design of informed heuristics, inference methods and efficient search strategies. Experimental results on common benchmarks in the field permit to assess the components and search strategies of FAPE, and to compare it to IPC planners. The results show the proposed approach to be competitive with less expressive planners and often superior when hierarchical control knowledge is provided. FAPE, a freely available system, provides other features, not covered here, such as the integration of planning with acting, and the handling of sensing actions in partially observable environments.
Code (1)
Similar Papers 제목 키워드 기반
FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration
All-in-One Image Restoration (AIO-IR) aims to develop a unified model that can handle multiple degradations under complex conditions. However, existing methods often rely on task-specific designs or latent routing strate…
Zero-shot GeneralizationImage RestorationTowards Urban Planing AI Agent in the Age of Agentic AI
Generative AI, large language models, and agentic AI have emerged separately of urban planning. However, the convergence between AI and urban planning presents an interesting opportunity towards AI urban planners. Existi…
SafePilot: A Framework for Assuring LLM-enabled Cyber-Physical Systems
Large Language Models (LLMs), deep learning architectures with typically over 10 billion parameters, have recently begun to be integrated into various cyber-physical systems (CPS) such as robotics, industrial automation,…
TempAct: Advancing Temporal Plausibility in Autoregressive Video Generation via Planner-Executor RL
Autoregressive (AR) video diffusion models enable low-latency streaming generation by synthesizing videos chunk by chunk with cached visual context, but this chunk-wise formulation makes temporal instruction following am…
Reinforcement LearningInstruction FollowingVideo GenerationAHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising
In online advertising, the inherent complexity and dynamic nature of advertising environments necessitate the use of auto-bidding services to assist advertisers in bid optimization. This complexity is further compounded …
Reinforcement Learning