Decision-Theoretic Planning in the Graphplan Framework (original) (raw)
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The goal of robotics research is to design a robot to fulfill a variety of tasks in the real world. Inherent in the real world is a high degree of uncertainty about the robot's behavior and about the world. We introduce a robot task architecture, DTRC, that generates plans with actions that incorporate costs and uncertain effects, and states that yield rewards.
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This paper presents a conceptual architecture for autonomous robots that integrates behaviour-based and goal-directed action as by following a traditional action plan. Dual Dynamics is the formalism for describing behaviourbased action. Partial-order propositional plans, which get generated by GRAPHPLAN , are used as a basis for acting goal-directedly; the concept is suitable for using other planning methods and plan representations, though. The paper presents the corresponding action and plan representations at the plan side and at the behaviour side. Moreover, it describes how behaviour-based action is biased towards executing a plan and how information from the behaviour side is fed back to the plan side to determine progress in the plan execution.
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This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate explicit and symbolic, approximate and optimal heuristic search planners. Selected experiments highlight that the symbolic representation yields much larger and more accurate pattern databases than the ones generated with explicit methods.
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C ontrol has to do with the intelligent, adaptive execution of a piece of a task, or an action, and with its interaction with the environment; at the same time, it copes with the disturbances coming from the external world (i.e., with the "struggle of the world" against our intentions). Planning has to do with the definition of sequences of actions, or tasks, to attain complex goals. The relation between planning and control is traditionally considered hierarchical: planning is performed at a higher level of abstraction as compared with control.
2017
LINEAR PLANNING LOGIC AND LINEAR LOGIC GRAPH PLANNER: DOMAIN INDEPENDENT TASK PLANNERS BASED ON LINEAR LOGIC Sıtar Kortik Ph.D. in Computer Engineering Advisor: Varol Akman Co-Advisor: Uluç Saranlı September, 2017 Linear Logic is a non-monotonic logic, with semantics that enforce single-use assumptions thereby allowing native and efficient encoding of domains with dynamic state. Robotic task planning is an important example for such domains, wherein both physical and informational components of a robot’s state exhibit non-monotonic properties. We introduce two novel and efficient theorem provers for automated construction of proofs for an exponential multiplicative fragment of linear logic to encode deterministic STRIPS planning problems in general. The first planner we introduce is Linear Planning Logic (LPL), which is based on the backchaining principle commonly used for constructing logic programming languages such as Prolog and Lolli, with a novel extension for LPL to handle pro...
Graph Based Representation of Dynamic Planning
profit, time, pleasure, etc.) by achieving this goal and consequently to define the efficient actions for this end. Abstract. Dynamic planning concerns the planning and execution of actions in a dynamic, real world environment. Its goal is to take into account changes generated by unpredicted events occurred during the execution of actions. In this paper we develop the theoretic model of dynamic planning presented in . This model proposes a graph representation of possible, efficient and best plans of agents acting in a dynamic environment. Agents have preferences among consequences of their possible actions performed to reach a fixed goal. Environmental changes and their consequences are taken into account by several approaches proposed in the so-called "reactive planning" field. The dynamic planning approach we propose, handles in addition changes on agents´ preferences and on their methods to evaluate them; it is modeled as a multi-objective dynamic programming problem.
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2003
We examine the approach of encoding planning problems as CSPs more closely. First we present a simple CSP encoding for planning problems and then a set of transformations that can be used to eliminate variables and add new constraints to the encoding. We show that our transformations uncover additional structure in the planning problem, structure that subsumes the structure uncovered by GRAPHPLAN planning graphs. We solve the CSP encoded planning problem by using standard CSP algorithms. Empirical evidence is presented to validate the effectiveness of this approach to solving planning problems, and to show that even a prototype implementation is more effective than standard GRAPHPLAN. Our prototype is even competitive with far more optimized planning graph based implementations. We also demonstrate that this approach can be more easily lifted to more complex types of planning than can planning graphs. In particular, we show that the approach can be easily extended to planning with resources.
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Artificial Intelligence Review, 2023
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.