An Artificial Intelligence (AI) agent acting in an environment can perceive the environment through sensors and execute actions through actuators. Symbolic planning provides an agent with decision-making capabilities about the actions to execute for accomplishing tasks in the environment. For applying symbolic planning, an agent needs to know its symbolic state, and an abstract model of the environment dynamics. However, in the real world, an agent has low-level perceptions of the environment (e.g. its position given by a GPS sensor), rather than symbolic observations representing its current state. Furthermore, in many real-world scenarios, it is not feasible to provide an agent with a complete and correct model of the environment, e.g., when the environment is unknown a priori. The gap between the high-level representations, suitable for symbolic planning, and the low-level sensors and actuators, available in a real-world agent, can be bridged by integrating learning, planning, and acting. Firstly, an agent has to map its continuous perceptions into its current symbolic state, e.g. by detecting the set of objects and their properties from an RGB image provided by an onboard camera. Afterward, the agent has to build a model of the environment by interacting with the environment and observing the effects of the executed actions. Finally, the agent has to plan on the learned environment model and execute the symbolic actions through its actuators. We propose an architecture that integrates learning, planning, and acting. Our approach combines data-driven learning methods for building an environment model online with symbolic planning techniques for reasoning on the learned model. In particular, we focus on learning the environment model, from either continuous or symbolic observations, assuming the agent perceptual input is the complete and correct state of the environment, and the agent is able to execute symbolic actions in the environment. Afterward, we assume a partial model of the environment and the ...


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Integrating Planning and Learning for Agents Acting in Unknown Environments



    Erscheinungsdatum :

    2023-04-27


    Medientyp :

    Hochschulschrift


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Learning Optimal Guidance Behavior in Unknown Environments within Receding Horizon Planning

    Verma, A. / Mettler, B. / American Helicopter Society | British Library Conference Proceedings | 2014



    Invisible Control of Self-Organizing Agents Leaving Unknown Environments

    Albi, Giacomo / Bongini, Mattia / Cristiani, Emiliano et al. | BASE | 2016

    Freier Zugriff

    Path planning for autonomous driving in unknown environments

    Dolgov, Dmitri / Thrun, Sebastian / Montemerlo, Michael et al. | Tema Archiv | 2009