This requires, for Goal-driven autonomy (GDA) is a reflective model example, experts to anticipate what discrepancies can occur, of goal reasoning that controls the focus of an identify what goals can be formulated, and define their agent's planning activities by dynamically relative priority. However, few techniques have been resolving unexpected discrepancies in the world investigated for learning this knowledge, and those that do state, which frequently arise when solving tasks in learn only goal formulation knowledge (Weber et al. 2010; complex environments. GDA agents have Powell et al. 2011). This can be problematic; while these performed well on such tasks by integrating agents may perform well in simple environments, in others a methods for discrepancy recognition, explanation, domain expert might not know the (state) expectations for goal formulation, and goal management. However, executing every action in every state, nor which goal should they require substantial domain knowledge, be pursued to resolve every possible discrepancy, or even including what constitutes a discrepancy and how the space of all possible discrepancies.

Robots must perform tasks efficiently and reliably while acting underuncertainty. One way to achieve efficiency is to give the robot common-sense knowledge about the structure of the world. Reliable robot behaviour can be achieved by modelling the uncertaintyin the world probabilistically. We present a robot system that combines these two approaches and demonstrate the improvements in efficiency and reliability that result. Our first contribution is a probabilistic relational model integrating common-sense knowledge about the world in general, with observations of a particular environment. Our second contribution is a continual planning system which is able to plan in the large problems posed by that model, by automatically switching between decision-theoretic and classical procedures. We evaluate our system on object search tasks in two different real-world indoor environments. By reasoning about the trade-offs between possible courses of action with different informational effects, and exploiting the cues and general structures of those environments, our robot is able to consistently demonstrate efficient and reliable goal-directed behaviour.

In an introductory engineering course with an annual enrollment of over 1000 students, a professor has little option but to rely on multiple choice exams for midterms and finals. Furthermore, the teaching assistants are too overloaded to give detailed feedback on submitted homework assignments. We introduce Mechanix, a computer-assisted tutoring system for engineering students. Mechanix uses recognition of freehand sketches to provide instant, detailed, and formative feedback as the student progresses through each homework assignment, quiz, or exam. Free sketch recognition techniques allow students to solve free-body diagram and static truss problems as if they were using a pen and paper. The same recognition algorithms enable professors to add new unique problems simply by sketching out the correct answer. Mechanix is able to ease the burden of grading so that instructors can assign more free response questions, which provide a better measure of student progress than multiple choice questions do.

LogAnswer is a question answering (QA) system for the German language, aimed at providing concise and correct answers to arbitrary questions. For this purpose LogAnswer is designed as an embedded artificial intelligence system which integrates methods from several fields of AI, namely natural language processing, machine learning, knowledge representation and automated theorem proving. We intend to employ LogAnswer as a virtual user within Internet-based QA forums, where it must be able to identify the questions that it cannot answer correctly, a task that normally receives little attention in QA research compared to the actual answer derivation. The paper presents a machine learning solution to the wrong answer avoidance (WAA) problem, applying a meta classifier to the output of simple term-based classifiers and a rich set of other WAA features. Experiments with a large set of real-world questions from a QA forum show that the proposed method significantly improves the WAA characteristics of our system.

It is well known that the three water categories science. Methods in biophysics only provide qualitative have different functions. Individually bound water has multiple description of the structure and thus clarifying contacts that stabilize the protein structure. Hydration the collective phenomena of a huge number water has heterogeneous dynamical behavior, contributing to of water molecules is still beyond intuition protein folding, stability and dynamics, and interacting with in biophysics. We introduce a simulation-based the bulk water. Bulk water is free to move and continuously data mining approach that quantitatively model the exchanges with hydration water, and indirectly influences on structure of water surrounding a protein as clusters the protein [Bizzarri and Cannistraro, 2002], [Halle, 2004]. of water molecules having similar moving behavior. Much effort has been devoted to quantitatively model the The paper presents and explains how the advances relative motion (orientation, rotation and velocity) and dynamical of AI technique can potentially solve this properties of individual water molecules in protein challenging data-intensive problem.

Statutory records of underground utility apparatus (such as pipes andcables) are notoriously inaccurate, so street surveys are usually undertakenbefore road excavation takes place to minimize the extent and duration ofexcavation and for health and safety reasons. This involves the use ofsensors such as Ground Penetrating Radar (GPR). The GPR scans are thenmanually interpreted and combined with the expectations from the utilityrecords and other data such as surveyed manholes. The task is complex owingto the difficulty in interpreting the sensor data, and the spatialcomplexity and extent of under street assets. We explore the application ofAI techniques, in particular Bayesian data fusion (BDF), to automaticallygenerate maps of buried apparatus. Hypotheses about the spatial location anddirection of buried assets are extracted by identifying hyperbolae in theGPR scans. The spatial location of surveyed manholes provides further inputto the algorithm, as well as the prior expectations from the statutoryrecords. These three data sources are used to produce the most probable mapof the buried assets. Experimental results on real and simulated data setsare presented.

This paper presents a new Monte-Carlo search algorithm for very large sequential decision-making problems. We apply non-linear regression within Monte-Carlo search, online, to estimate a state-action value function from the outcomes of random roll-outs. This value function generalizes between related states and actions, and can therefore provide more accurate evaluations after fewer rollouts. A further significant advantage of this approach is its ability to automatically extract and leverage domain knowledge from external sources such as game manuals. We apply our algorithm to the game of Civilization II, a challenging multi-agent strategy game with an enormous state space and around 10^21 joint actions. We approximate the value function by a neural network, augmented by linguistic knowledge that is extracted automatically from the official game manual. We show that this non-linear value function is significantly more efficient than a linear value function, which is itself more efficient than Monte-Carlo tree search. Our non-linear Monte-Carlo search wins over 78% of games against the built-in AI of Civilization II.

Deep space missions are characterized by severely constrained communication links. To meet the needs of future missions and increase their scientific return, future space systems will require an increased level of autonomy on-board. In this work, we propose a comprehensive approach to on-board autonomy relying on model-based reasoning, and encompassing many important reasoning capabilities such as plan generation, validation, execution and monitoring, FDIR, and run-time diagnosis. The controlled platform is represented symbolically, and the reasoning capabilities are seen as symbolic manipulation of such formal model. We have developed a prototype of our framework, implemented within an on-board Autonomous Reasoning Engine. We have evaluated our approach on two case-studies inspired by real-world, ongoing projects, and characterized it in terms of reliability, availability and performance.

Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and learning is here proposed to satisfy these strong demands. The approach rapidly learns planning operators from few action experiences using a competitive strategy where many alternatives of cause-effect explanations are evaluated in parallel, and the most successful ones are used to generate the operators. The success of a cause-effect explanation is evaluated by a probabilistic estimate that compensates the lack of experience, producing more confident estimations and speeding up the learning in relation to other known estimates. The system operates without task interruption by integrating in the planning-learning loop a human teacher that supports the planner in making decisions. All the mechanisms are integrated and synchronized in the robot using a general decision-making framework. The feasibility and scalability of the architecture are evaluated in two different robot platforms: a Stäubli arm, and the humanoid ARMAR III.

This paper identifies a widely existing phenomenon in web data, which we call the "words of few mouths" phenomenon. This phenomenon, in the context of online reviews, refers to the case that a large fraction of the reviews are each voted only by very few users. We discuss the challenges of "words of few mouths" in the development of recommender systems based on users' opinions and advocate probabilistic methodologies to handle such challenges. We develop a probabilistic model and correspondingly a logistic regression based learning algorithm for review helpfulness prediction. Our experimental results indicate that the proposed model outperforms the current state-of-the-art algorithms not only in the presence of the "words of few mouths" phenomenon, but also in the absence of such phenomena.