Author name cluster

Thomas Geier

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

AAAI Conference 2015 Conference Paper

A Planning-Based Assistance System for Setting Up a Home Theater

Pascal Bercher
Felix Richter
Thilo Hörnle
Thomas Geier
Daniel Höller
Gregor Behnke
Florian Nothdurft
Frank Honold

Modern technical devices are often too complex for many users to be able to use them to their full extent. Based on planning technology, we are able to provide advanced user assistance for operating technical devices. We present a system that assists a human user in setting up a complex home theater consisting of several HiFi devices. For a human user, the task is rather challenging due to a large number of different ports of the devices and the variety of available cables. The system supports the user by giving detailed instructions how to assemble the theater. Its performance is based on advanced user-centered planning capabilities including the generation, repair, and explanation of plans.

PDF Details

UAI Conference 2015 Conference Paper

Locally Conditioned Belief Propagation

Thomas Geier
Felix Richter 0001
Susanne Biundo

Conditioned Belief Propagation (CBP) is an algorithm for approximate inference in probabilistic graphical models. It works by conditioning on a subset of variables and solving the remainder using loopy Belief Propagation. Unfortunately, CBP’s runtime scales exponentially in the number of conditioned variables. Locally Conditioned Belief Propagation (LCBP) approximates the results of CBP by treating conditions locally, and in this way avoids the exponential blow-up. We formulate LCBP as a variational optimization problem and derive a set of update equations that can be used to solve it. We show empirically that LCBP delivers results that are close to those obtained from CBP, while the computational cost scales favorably with problem size.

Details

ECAI Conference 2014 Conference Paper

Conditioned Belief Propagation Revisited

Thomas Geier
Felix Richter 0001
Susanne Biundo

Belief Propagation (BP) applied to cyclic problems is a well known approximate inference scheme for probabilistic graphical models. To improve the accuracy of BP, a divide-and-conquer approach termed Conditioned Belief Propagation (CBP) has been proposed in the literature. It recursively splits a problem by conditioning on variables, applies BP to subproblems, and merges the results to produce an answer to the original problem. In this essay, we propose a reformulated version of CBP that exhibits anytime behavior, and allows for more specific tuning by formalizing a further decision point that decides which subproblem is to be decomposed next. We propose some simple and easy to compute heuristics, and demonstrate their performance using an empirical evaluation on randomly generated problems.

Details

ICAPS Conference 2014 Conference Paper

Plan, Repair, Execute, Explain - How Planning Helps to Assemble your Home Theater

Pascal Bercher
Susanne Biundo
Thomas Geier
Thilo Hoernle
Florian Nothdurft
Felix Richter 0001
Bernd Schattenberg

In various social, work-related, or educational contexts, an increasing demand for intelligent assistance systems can be observed. In this paper, we present a domain-independent approach that combines a number of planning and interaction components to realize advanced user assistance. Based on a hybrid planning formalism, the components provide facilities including the generation, execution, and repair as well as the presentation and explanation of plans. We demonstrate the feasibility of our approach by means of a system that aims to assist users in the assembly of their home theater. An empirical evaluation shows the benefit of such a supportive system, in particular for persons with a lack of domain expertise.

Details

ECAI Conference 2012 Conference Paper

Exploiting Expert Knowledge in Factored POMDPs

Felix Müller
Christian Späth
Thomas Geier
Susanne Biundo

Decision support in real-world applications is often challenging because one has to deal with large and only partially observable domains. In case of full observability, large deterministic domains are successfully tackled by making use of expert knowledge and employing methods like Hierarchical Task Network (HTN) planning. In this paper, we present an approach that transfers the advantages of HTN planning to partially observable domains. Experimental results for two implemented algorithmsUCTA* search, show that our approach significantly speeds up the generation of high-quality policies: the policies generated by our approach consistently outperform policies generated by Symbolic Perseus and can be computed in less than 10% of its runtime on average.

Details

IJCAI Conference 2011 Conference Paper

On the Decidability of HTN Planning with Task Insertion

Thomas Geier
Pascal Bercher

The field of deterministic AI planning can roughly be divided into two approaches - classical state-based planning and hierarchical task network (HTN) planning. The plan existence problem of the former is known to be decidable while it has been proved undecidable for the latter. When extending HTN planning by allowing the unrestricted insertion of tasks and ordering constraints, one obtains a form of planning which is often referred to as "hybrid planning. " We present a simplified formalization of HTN planning with and without task insertion. We show that the plan existence problem is undecidable for the HTN setting without task insertion and that it becomes decidable when allowing task insertion. In the course of the proof, we obtain an upper complexity bound of EXPSPACE for the plan existence problem for propositional HTN planning with task insertion.

PDF Details DOI