The increasing variety of robotic systems create the need for flexible architectures enabling easy integration of new robot configurations into existing multi-robot systems. This requires methods for general reasoning about what different robots are capable of doing. Teamwork is a very important factor in complex, dynamic domains. In heterogeneous teams, robustness and flexibility are increased by the diversity of the robots, each contributing different capabilities. Consequently it is reasonable to explicitly take the robots' capabilities into account when determining which robot is best suited for a task. This work develops a framework that formalizes robots' capabilities, relating to hard- and software configurations and providing a means to estimate a robot's suitability for a task. A learning algorithm for robot capabilities is included.

Since it is a relatively new concept, little is known about the computational complexity of fuzzy answer set programming (FASP) and almost no techniques are available to compute answer sets of FASP programs. Furthermore, the connections of FASP to other paradigms of nonmonotonic reasoning with continuous values are largely unexplored. In our disertation, we contribute to the ongoing research on FASP on two different levels: complexity and connections to fuzzy modal logics.

The problem of finding the set of pareto optimals for constraints and qualitative preferences together is of great interest to many application areas. It can be viewed as a preference constrained optimization problem where the goal is to find one or more feasible solutions that are not dominated by other feasible outcomes. Our work aims to enhance the current literature of the problem by providing solving methods targeting the problem in a dynamic environments. We target the problem with an eye on adopting and benefiting from the current constraint satisfaction techniques.

User-Centered Programming by Demonstration is an approach that places the needs of people above algorithmic constraints and requirements. In this paper we present a user-centered programming by demonstration project for authoring interactive robotic locomotion style. The style in which a robot moves about a space, expressed through its motions, can be used for communication. For example, a robot could move aggressively in reaction to a person’s actions, or alternatively react using careful, submissive movements. We present a new demonstration interface, algorithm, and evaluation results.

We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the agents' operational know-how, as defined by their libraries of abstract plans. In our logic, it is possible to refer to "rational" strategies for agents developed under the Belief-Desire-Intention agent paradigm. This allows us to express and verify properties of BDI systems using ATL-type logical frameworks.

The community-based generation of content has been tremendously successful in the World Wide Web — people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. RoboEarth is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform.

We present probabilistic Symbol-Refined Tree Substitution Grammars (SR-TSG) for statistical parsing of natural language sentences. An SR-TSG is an extension of the conventional TSG model where each nonterminal symbol can be refined (subcategorized) to fit the training data. Our probabilistic model is consistent based on the hierarchical Pitman-Yor Process to encode backoff smoothing from a fine-grained SR-TSG to simpler CFG rules, thus all grammar rules can be learned from training data in a fully automatic fashion. Our SR-TSG parser achieves the state-of-the-art performance on the Wall Street Journal (WSJ) English Penn Treebank data.

In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the problem we propose a new CF approach, Collaborative Less-is-More Filtering (CLiMF). In CLiMF the model parameters are learned by directly maximizing the Mean Reciprocal Rank (MRR), which is a well-known information retrieval metric for capturing the performance of top-k recommendations. We achieve linear computational complexity by introducing a lower bound of the smoothed reciprocal rank metric. Experiments on two social network datasets show that CLiMF significantly outperforms a naive baseline and two state-of-the-art CF methods.

Evaluating the quality of ranking functions is a core task in web search and other information retrieval domains. Because query distributions and item relevance change over time, ranking models often cannot be evaluated accurately on held-out training data. Instead, considerable effort is spent on manually labeling the relevance of query results for test queries in order to track ranking performance. We address the problem of estimating ranking performance as accurately as possible on a fixed labeling budget. Estimates are based on a set of most informative test queries selected by an active sampling distribution. Query labeling costs depend on the number of result items and item-specific attributes such as document length. We derive cost-optimal sampling distributions for commonly used ranking performance measures. Experiments on web search engine data illustrate significant reductions in labeling costs.

Given a set of data, recorded by observing the decisions of an expert player, we present a case-based framework that allows the successful generalisation of those decisions in the game of no limit Texas Hold'em. We address the problems of determining a suitable action abstraction and the resulting state translation that is required to map real-value bet amounts into a discrete set of abstract actions. We also detail the similarity metrics used in order to identify similar scenarios, without which no generalisation of playing decisions would be possible. We show that we were able to successfully generalise no limit betting decisions from recorded data via our agent, SartreNL, which achieved a 5th place finish out of 11 opponents at the 2012 Annual Computer Poker Competition.