If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Awaad, Iman (Bonn-Rhein-Sieg University of Applied Sciences) | Kraetzschmar, Gerhard (Bonn-Rhein-Sieg University of Applied Sciences) | Hertzberg, Joachim (Osnabrück University and DFKI RIC Osnabrück Branch)
Adapting plans to changes in the environment by finding alternatives and taking advantage of opportunities is a common human behavior. The need for such behavior is often rooted in the uncertainty produced by our incomplete knowledge of the environment. While several existing planning approaches deal with such issues, artificial agents still lack the robustness that humans display in accomplishing their tasks. In this work, we address this brittleness by combining Hierarchical Task Network planning, Description Logics, and the notions of affordances and conceptual similarity. The approach allows a domestic service robot to find ways to get a job done by making substitutions. We show how knowledge is modeled, how the reasoning process is used to create a constrained planning problem, and how the system handles cases where plan generation fails due to missing/unavailable objects. The results of the evaluation for two tasks in a domestic service domain show the viability of the approach in finding and making the appropriate goal transformations.
Rockel, Sebastian (University of Hamburg) | Neumann, Bernd (University of Hamburg) | Zhang, Jianwei (University of Hamburg) | Dubba, Sandeep Krishna Reddy (University of Leeds) | Cohn, Anthony G. (University of Leeds) | Konecny, Stefan (Örebro University) | Mansouri, Masoumeh (Örebro University) | Pecora, Federico (Örebro University) | Saffiotti, Alessandro (Örebro University) | Günther, Martin (University of Osnabrück) | Stock, Sebastian (University of Osnabrück) | Hertzberg, Joachim (University of Osnabrück) | Tome, Ana Maria (University of Aveiro ) | Pinho, Armando (University of Aveiro) | Lopes, Luis Seabra (University of Aveiro ) | Riegen, Stephanie von (HITeC e.V. ) | Hotz, Lothar (HITeC e.V.)
One way to improve the robustness and flexibility of robot performance is to let the robot learn from its experiences. In this paper, we describe the architecture and knowledge-representation framework for a service robot being developed in the EU project RACE, and present examples illustrating how learning from experiences will be achieved. As a unique innovative feature, the framework combines memory records of low-level robot activities with ontology-based high-level semantic descriptions.