This article offers a literature review of goalkeeper robots in the context of the RoboCupSoccer competition. The latter is one of the various league categories hosted by the RoboCup Federation, which fosters AI and Robotics with their landmark challenges. Despite the number of articles on the subject of the goalkeeper, there is a lack of studies offering a comprehensive and up-to-date analysis. We propose to provide a review of research related to goalkeepers within the RoboCupSoccer leagues in order to extract possible improvements and scientific issues. The goalkeeper, although being a specific player, has many skills in common with other players. Therefore, this review is divided into three parts: perception, cognition and action, where the perception and action parts are common to all players and the cognition part focuses on goalkeepers. The discussion will open up on the possible improvements of the developments made for these goalkeepers.

Partial subtle mirroring of nonverbal behaviors during conversations (also known as mimicking or parallel empathy), is essential for rapport building, which in turn is essential for optimal human-human communication outcomes. Mirroring has been studied in interactions between robots and humans, and in interactions between Embodied Conversational Agents (ECAs) and humans. However, very few studies examine interactions between humans and ECAs that are integrated with robots, and none of them examine the effect of mirroring nonverbal behaviors in such interactions. Our research question is whether integrating an ECA able to mirror its interlocutor's facial expressions and head movements (continuously or intermittently) with a human-service robot will improve the user's experience with the support robot that is able to perform useful mobile manipulative tasks (e.g. at home). Our contribution is the complex integration of an expressive ECA, able to track its interlocutor's face, and to mirror his/her facial expressions and head movements in real time, integrated with a human support robot such that the robot and the agent are fully aware of each others', and of the users', nonverbals cues. We also describe a pilot study we conducted towards answering our research question, which shows promising results for our forthcoming larger user study.

Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSC's that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI, allowing it to generate much simpler and smaller concepts that are specific-enough to answer a given query. With independence between computed MSC's, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries.

We combined a spoken dialog system that we developed to deliver brief health interventions with the fully autonomous humanoid robot (NAO). The dialog system is based on a framework facilitating Markov decision processes (MDP). It is optimized using reinforcement learning (RL) algorithms with data we collected from real user interactions. The system begins to learn optimal dialog strategies for initiative selection and for the type of confirmations that it uses during theinteraction. The health intervention, delivered by a 3D character instead of the NAO, has already been evaluated, with positive results in terms of task completion, ease of use, and future intention to use the system.  The current spoken dialog system for the humanoid robot is a novelty and exists so far as a proof ofconcept.

Instance checking is considered a central service for data retrieval from description logic (DL) ontologies. In this paper, we propose a revised most specific concept (MSC) method for DL SHI}, which converts instance checking into subsumption problems. This revised method can generate small concepts that are specific-enough to answer a given query, and allow reasoning to explore only a subset of the ABox data to achieve efficiency. Experiments show effectiveness of our proposed method in terms of concept size reduction and the improvement in reasoning efficiency.

The extraction of logically-independent fragments out of an ontology ABox can be useful for solving the tractability problem of querying ontologies with large ABoxes. In this paper, we propose a formal definition of an ABox module, such that it guarantees complete preservation of facts about a given set of individuals, and thus can be reasoned independently w.r.t. the ontology TBox. With ABox modules of this type, isolated or distributed (parallel) ABox reasoning becomes feasible, and more efficient data retrieval from ontology ABoxes can be attained. To compute such an ABox module, we present a theoretical approach and also an approximation for $\mathcal{SHIQ}$ ontologies. Evaluation of the module approximation on different types of ontologies shows that, on average, extracted ABox modules are significantly smaller than the entire ABox, and the time for ontology reasoning based on ABox modules can be improved significantly.

Collecting and maintaining accurate world knowledge in a dynamic, complex, adversarial, and stochastic environment such as the RoboCup 3D Soccer Simulation is a challenging task. Knowledge should be learned in real-time with time constraints. We use recently introduced Off-Policy Gradient Descent algorithms within Reinforcement Learning that illustrate learnable knowledge representations for dynamic role assignments. The results show that the agents have learned competitive policies against the top teams from the RoboCup 2012 competitions for three vs three, five vs five, and seven vs seven agents. We have explicitly used subsets of agents to identify the dynamics and the semantics for which the agents learn to maximize their performance measures, and to gather knowledge about different objectives, so that all agents participate effectively and efficiently within the group.

Will we see autonomous humanoid robots that play (and win) soccer against the human soccer world champion in the year 2050? There are serious research questions that have to be tackled behind the scenes of a soccer game: perception, decision making, action selection, hardware design, materials, energy, and more. RoboCup is also about the nature of intelligence, and playing soccer acts as a performance measure of systems that contain artificial intelligence -- in much the same way chess has been used over the last century. This article outlines the current situation following 10 years of research with reference to the results of the 2006 World Championship in Bremen, Germany, and discusses future challenges.

Will we see autonomous humanoid robots that play (and win) soccer against the human soccer world champion in the year 2050? This question is not easy to answer, and the idea is quite visionary. However, this is the goal of the RoboCup Federation. There are serious research questions that have to be tackled behind the scenes of a soccer game: perception, decision making, action selection, hardware design, materials, energy, and more. RoboCup is also about the nature of intelligence, and playing soccer acts as a performance measure of systems that contain artificial intelligence -- in much the same way chess has been used over the last century. This article outlines the current situation following 10 years of research with reference to the results of the 2006 World Championship in Bremen, Germany, and discusses future challenges.

Ohio State University) focused on the use of case-based reasoning for both planning and world modeling. Nicola Muscettola (NASA Ames) focused on reactive behaviors. Laboratory) described an approach Within this general theme, to planning with multiagent the aim was to bring together researchers execution. The presentation ecent developments in multiagent shown promising results in the robotics, intelligent autonomous of Thomas Wagner (University of modeling of autonomous, collaborative vehicles). The common denominator Brement), Christoph Schlieder (University behavior between agents in different that these groups share is the pragmatic of Bamberg), and Ubbo Visser environments.