A Unified Framework Based on HTN and POP Approaches for Multi-Agent Planning

Minotra, Dev; Liu, Jiming

doi:10.1109/iat.2007.30

Cited by 4 publications

(3 citation statements)

References 16 publications

(4 reference statements)

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“…The multi-agent systems based on hierarchical task networks (HTN) (Obst and Boedecker 2006;Dix et al 2003;Clement, Durfee, and Barrett 2007;Pellier and Fiorino 2007;Cardoso and Bordini 2019;Kuter and Hamell 2018), although have hierarchical deliberations, use abstract descriptive models. Compared to operational models that are used in Dec-RPAE, descriptive models (e.g., a classical precondition-and-effects action models) tell what the action will do, but not how to do it.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Refinement Planning and Acting

Patra

Nau

2021

ICAPS

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We describe Dec-RPAE, a system for decentralized multi-agent acting and planning in partially observable and non-deterministic environments. The system includes both an acting component and an online planning component. The acting component is similar to RAE, a well-known acting engine, but incorporates changes that enable it to be used by multiple autonomous agents working independently in a collaborative setting. Each agent runs a local copy of Dec-RPAE, with a set of hierarchical refinement methods using operational models that specify various ways to accomplish its designated tasks. To perform actions, the agent uses Dec-RPAE’s acting component to execute the methods in the agent’s environment. To advise the acting component on which method to execute, the planning component repeatedly does Monte Carlo simulations of the methods to estimate their potential outcomes. Agents can communicate with each other to exchange information about their states, tasks, goals, and plans in order to cooperatively succeed in their respective missions. Our experimental results demonstrate that Dec-RPAE is useful for improving the agents’ performances.

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Section: Related Workmentioning

confidence: 99%

Decentralized Refinement Planning and Acting

Patra

Nau

2021

ICAPS

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“…For instance, SHOP was extended to generate partial order plans [30], temporal plans [31] or plans integrating user preferences [32]. It was also extended to multi-agent systems [33], to on-line search [34] or to solve non-deterministic planning problems [35] by combining Binary Decision Diagram and HTN-based mechanisms in order to constrain the search of a solution plan. GoDel planning system [36] synthesizes classical and HTN planning.…”

Section: Related Workmentioning

confidence: 99%

Grounding of HTN Planning Domain

Ramoul

Pellier

Fiorino

et al. 2017

Int. J. Artif. Intell. Tools

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Many Artificial Intelligence techniques have been developed for intelligent and autonomous systems to act and make rational decisions based on perceptions of the world state. Among these techniques, HTN (Hierarchical Task Network) planning is one of the most used in practice. HTN planning is based on expressive languages allowing to specify complex expert knowledge for real world domains. At the same time, many preprocessing techniques for classical planning were proposed to speed up the search. One of these technique, named grounding, consists in enumerating and instantiating all the possible actions from the planning problem descriptions. This technique has proven its effectiveness. Therefore, combining the expressiveness of HTN planning with the efficiency of the grounding preprocessing techniques used in classical planning is a very challenging issue. In this paper, we propose a generic algorithm to ground the domain representation for HTN planning. We show experimentally that grounding process improves the performances of state of the art HTN planners on a range of planning problems from the International Planning Competition (IPC).

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“…Recent works in htn have proposed stratified planning where remote agents plan subtasks and report them to a master. In [12], finding the final plan is the result of the exchange of proposals and counter proposals between agents. In [13], the child agents are also responsible for execution, and interleave planning, execution, and replanning.…”

Section: Related Workmentioning

confidence: 99%

Planner9, a HTN Planner Distributed on Groups of Miniature Mobile Robots

Magnenat

Voelkle

Mondada

2009

Intelligent Robotics and Applications

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Abstract. Autonomous mobile robots are promising tools for operations in environments that are difficult to access for humans. When these environments are dynamic and non-deterministic, like in collapsed buildings, the robots must coordinate their actions and the use of resources using planning. This paper presents Planner9, a hierarchical task network (htn) planner that runs on groups of miniature mobile robots. These robots have limited computational power and memory, but are well connected through Wi-Fi. Planner9 takes advantage of this connectivity to distribute the planning over different robots. We have adapted the htn algorithm to perform parallel search using A* and to limit the number of search nodes through lifting. We show that Planner9 scales well with the number of robots, even on non-linear tasks that involve recursions in their decompositions. We show that contrary to JSHOP2, Planner9 finds optimal plans.

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A Unified Framework Based on HTN and POP Approaches for Multi-Agent Planning

Cited by 4 publications

References 16 publications

Decentralized Refinement Planning and Acting

Decentralized Refinement Planning and Acting

Grounding of HTN Planning Domain

Planner9, a HTN Planner Distributed on Groups of Miniature Mobile Robots

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