|TU01||Hector Geffner (UPF Barcelona, Spain)|
|AI Planning: the model-based approach to intelligent behavior and a lab for representation and reasoning techniques|
|Planning is concerned with
the development of solvers for a wide range of problems involving the
selection of actions for achieving goals. In these problems,
actions may be deterministic or not, full or partial sensing may be
available or not,
costs may be associated with actions, states, or both, and so no. A
range of closely related
state-models is used to make sense of these various classes of problems
and their (optimal) solutions,
while planning algorithms aim to compute such solutions (plans)
In the last few years, significant progress has been made in planning research, resulting in algorithms that can produce plans effectively in a variety of settings. The key component in these algorithms is inference in the form of heuristic estimators, constraint propagation, variable elimination, and the like. In all cases, knowledge that is implicit in the description of a planning problem is made explicit in order to focus the search for plans, in certain cases, bypassing the need to search altogether.
In this tutorial, I will review the most common planning models and the ideas underlying current planning algorithms, placing emphasis on the challenges ahead.
|TU02||Didier Dubois (IRIT, France)|
|Uncertainty in Knowledge Representation and Reasoning|
|Rather than focusing on a single
approach (like possibility theory),
the tutorial aims at providing a coherent picture of several topics
which have received considerable attention in the uncertainty field in
After the discussion of a typology of imperfect information
(uncertainty, variability, indiscernibility, fuzziness,
contradiction), we will discuss the following topics and their
|TU03||Franz Baader (TU Dresden, Germany)|
|Everything You Always Wanted to Know About Description Logics, But Were Afraid to Ask Your Ontology Engineer|
|Description Logics (DLs) are a
successful family of logic-based knowledge representation formalisms,
which can be used to represent the conceptual knowledge of an
application domain in a structured and formally well-understood way.
They are employed in various application domains, such as natural
language processing, configuration, and databases, but their most
notable success so far is the adoption of the DL-based language OWL as
standard ontology language for the semantic web.
This tutorial concentrates on designing and analyzing reasoning procedures for DLs. After a short introduction and a brief overview of the research of the last 15 years, it will on the one hand present approaches for reasoning in expressive DLs, which are the foundation for reasoning in OWL. On the other hand, it will consider tractable reasoning in a more light-weight DL, which is employed in bio-medical ontologies.
|TU04||Gal A. Kaminka (Bar Ilan University, Israel)|
|Situated Agent Teams: Getting Robots to Cooperate|
|Teams of situated agents are common in real-world and research domains, ranging from simulated pilots in commercial applications, through synthetic players in RoboCup competitions and computer games, to teams of unmanned vehicles moving in formation or covering an area together. Through the last 10 years, different teamwork mechanisms have been proposed, covering different challenges of joint decision-making in such dynamic, complex, domains. This tutorial will pull these different efforts together, attempting to synthesize a coherent framework for situated teamwork, draw conclusions as to lessons learned, and point the way to promising future prospects in this exciting area of research. In doing so, I will bring to bear my 10 years of experience in building virtual and physical robot teams in a numerous domains and applications, and in developing generic architectures for teamwork and coordination.|