ARPIA (2018)

Providing agents (both human and software) with recommendations on future behavior (actions, plans or goals) based on learning models of their past behavior and recognition of their present actions, plans or goals. In order to achieve the objective, we will integrate techniques from machine learning, activity/plan recognition and automated planning. The key idea is to analyze information from sensors in order to generate diverse models of an agent's behavior, such as planning domain models, probabilistic models, or generic classifiers. Read more about the project here.

<img class="img-fluid my-3 ml-2" title="Ministerio de Ciencia e Innovación" src="{static}/images/mci_feder.png" width="390"> <img class="img-fluid float-right my-3 ml-2" title="Ministerio de Ciencia e Innovación" src="{static}/images/logoARPIA-verysmall.png" width="350">

PI: Daniel Borrajo and Susana Fernández Arregui

Core team: PLG and GPRS AI Group Universidad Politécnica de Valencia

Funding: This project is granted by the Spanish Goverment (Ministerio de Economia y Empresa) and FEDER, UE funds under project TIN2017-88476-C2-2-R