141 research outputs found

    Anytime planning for agent behaviour

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    For an agent to act successfully in a complex and dynamic environment (such as a computer game)it must have a method of generating future behaviour that meets the demands of its environment. One such method is anytime planning. This paper discusses the problems and benefits associated with making a planning system work under the anytime paradigm, and introduces Anytime-UMCP (A-UMCP), an anytime version of the UMCP hierarchical task network (HTN) planner [Erol, 1995]. It also covers the necessary abilities an agent must have in order to execute plans produced by an anytime hierarchical task network planner

    SHOP2: An HTN Planning System

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    The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains

    Knowledge-Based Task Structure Planning for an Information Gathering Agent

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    An effective solution to model and apply planning domain knowledge for deliberation and action in probabilistic, agent-oriented control is presented. Specifically, the addition of a task structure planning component and supporting components to an agent-oriented architecture and agent implementation is described. For agent control in risky or uncertain environments, an approach and method of goal reduction to task plan sets and schedules of action is presented. Additionally, some issues related to component-wise, situation-dependent control of a task planning agent that schedules its tasks separately from planning them are motivated and discussed

    SHOP2: An HTN planning system

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    The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.open17833

    Hierarchical Goal Networks: Formalisms and Algorithms for Planning and Acting

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    In real-world applications of AI and automation such as in robotics, computer game playing and web-services, agents need to make decisions in unstructured environments that are open-world, dynamic and partially observable. In the AI and Robotics research communities in particular, there is much interest in equipping robots to operate with minimal human intervention in diverse scenarios such as in manufacturing plants, homes, hospitals, etc. Enabling agents to operate in these environments requires advanced planning and acting capabilities, some of which are not well supported by the current state of the art automated planning formalisms and algorithms. To address this problem, in my thesis I propose a new planning formalism that addresses some of the inadequacies in current planning frameworks, and a suite of planning and acting algorithms that operate under this planning framework. The main contributions of this thesis are: - Hierarchical Goal Network (HGN) Planning Formalism. This planning formalism combines aspects (and therefore harnesses advantages) of Classical Planning and Hierarchical Task Network (HTN) Planning, two of the most prominent planning formalisms currently in use. In particular, HGN planning algorithms, while retaining the efficiency and scalability advantages of HTNs, also allows incorporation of heuristics and other reasoning techniques from Classical Planning. - Planning Algorithms. Goal Decomposition Planner (GDP) and the Goal Decomposition with Landmarks (GoDeL) planner are two HGN planning algorithms that combines hierarchical decomposition with classical planning heuristics to outperform state-of-the-art HTN planners like SHOP and SHOP2. - Integration with Robotics. The Combined HGN and Motion Planning (CHaMP) algorithm integrates GoDeL with low-level motion and manipulation planning algorithms in Robotics to generate plans directly executable by robots. Given the need for autonomous agents to operate in open, dynamic and unstructured environments and the obvious need for high-level deliberation capabilities to enable intelligent behavior, the planning-and-acting systems that are developed as part of this thesis may provide unique insights into ways to realize these systems in the real world

    A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

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    The field of Sequential Decision Making (SDM) provides tools for solving Sequential Decision Processes (SDPs), where an agent must make a series of decisions in order to complete a task or achieve a goal. Historically, two competing SDM paradigms have view for supremacy. Automated Planning (AP) proposes to solve SDPs by performing a reasoning process over a model of the world, often represented symbolically. Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically. In the spirit of reconciliation, we provide a review of symbolic, subsymbolic and hybrid methods for SDM. We cover both methods for solving SDPs (e.g., AP, RL and techniques that learn to plan) and for learning aspects of their structure (e.g., world models, state invariants and landmarks). To the best of our knowledge, no other review in the field provides the same scope. As an additional contribution, we discuss what properties an ideal method for SDM should exhibit and argue that neurosymbolic AI is the current approach which most closely resembles this ideal method. Finally, we outline several proposals to advance the field of SDM via the integration of symbolic and subsymbolic AI

    Multiagent reactive plan application learning in dynamic environments

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    Using explanation structures to speed up local-search-based planning

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    Master'sMASTER OF ENGINEERIN

    Generating Instructions at Different Levels of Abstraction

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