Coordination in mixed agent-human environments is an important, yet not a simple, problem. Little attention has been given to the issues raised in teams that consist of both computerized agents and people. In such situations different considerations are in order, as people tend to make mistakes and they are affected by cognitive, social and cultural factors. In this paper we present a novel agent designed to proficiently coordinate with a human counterpart. The agent uses a neural network model that is based on a pre-existing knowledge base which allows it to achieve an efficient modeling of a human's decisions and predict their behavior. A novel communication mechanism which takes into account the expected effect of communication on the other member will allow communication costs to be minimized. In extensive simulations involving more than 200 people we investigated our approach and showed that our agent achieves better coordination when involved, compared to settings in which only humans or another state-of-the-art agent are involved.

Activity planning for missions such as the Mars Exploration Rover mission presents many technical challenges, including oversubscription, consideration of time, concurrency, resources, preferences, and uncertainty. These challenges have all been addressed by the research community to varying degrees, but significant technical hurdles still remain. In addition, the integration of these capabilities into a single planning engine remains largely unaddressed. However, I argue that there is a deeper set of issues that needs to be considered -- namely the integration of planning into an iterative process that begins before the goals, objectives, and preferences are fully defined. This introduces a number of technical challenges for planning, including the ability to more naturally specify and utilize constraints on the planning process, the ability to generate multiple qualitatively different plans, and the ability to provide deep explanation of plans.

The currently dominant approach to domain-independent planning is planning as heuristic search, with most successful planning heuristics being based on solutions to delete-relaxed versions of planning problems, in which the negative effects of actions are ignored. We introduce a principled, flexible, and practical technique for augmenting delete-relaxed tasks with a limited amount of delete information, by introducing special fluents that explicitly represent conjunctions of fluents in the original planning task. Differently from previous work, conditional effects are used to limit the growth of the task to be linear in the number of such conjunctions, making its use for obtaining heuristic functions feasible. The resulting heuristics are empirically evaluated, and shown to be some- times much more informative than standard delete-relaxation heuristics.

Traditional clustering algorithms are designed to search for a single clustering solution despite the fact that multiple alternative solutions might exist for a particular dataset. For example, a set of news articles might be clustered by topic or by the author's gender or age. Similarly, book reviews might be clustered by sentiment or comprehensiveness. In this paper, we address the problem of identifying alternative clustering solutions by developing a Probabilistic Multi-Clustering (PMC) model that discovers multiple, maximally different clusterings of a data sample. Empirical results on six datasets representative of real-world applications show that our PMC model exhibits superior performance to comparable multi-clustering algorithms.

A person demonstrates observed, and this technique is acceptable. However, the domains how to perform a task, such as making tea, by describing of these research efforts could be expanded if new the actions he or she carries out in front of the camera objects could be identified by their mention in descriptive and Kinect. RFID tags are placed on all relevant objects text, without any prior knowledge or mapping of the object that can accept them, and the subject wears an iBracelet on instance to a concept.

Query answering over Description Logic (DL) ontologies has become a vibrant field of research. Efficient realizations often exploit database technology and rewrite a given query to an equivalent SQL or Datalog query over a database associated with the ontology. This approach has been intensively studied for conjunctive query answering in the DL-Lite and EL families, but is much less explored for more expressive DLs and queries. We present a rewriting-based algorithm for conjunctive query answering over Horn-SHIQ ontologies, possibly extended with recursive rules under limited recursion as in DL+log. This setting not only subsumes both DL-Lite and EL, but also yields an algorithm for answering (limited) recursive queries over Horn-SHIQ ontologies (an undecidable problem for full recursive queries). A prototype implementation shows its potential for applications, as experiments exhibit efficient query answering over full Horn-SHIQ ontologies and benign downscaling to DL-Lite, where it is competitive with comparable state of the art systems.

Uniform random 3-SAT at the solubility phase transition is one of the most widely studied and empirically hardest distributions of SAT instances. For 20 years, this distribution has been used extensively for evaluating and comparing algorithms. In this work, we demonstrate that simple rules can predict the solubility of these instances with surprisingly high accuracy. Specifically, we show how classification accuracies of about 70% can be obtained based on cheaply (polynomial-time) computable features on a wide range of instance sizes. We argue in two ways that classification accuracy does not decrease with instance size: first, we show that our models' predictive accuracy remains roughly constant across a wide range of problem sizes; second, we show that a classifier trained on small instances is sufficient to achieve very accurate predictions across the entire range of instance sizes currently solvable by complete methods. Finally, we demonstrate that a simple decision tree based on only two features, and again trained only on the smallest instances, achieves predictive accuracies close to those of our most complex model. We conjecture that this two-feature model outperforms random guessing asymptotically; due to the model's extreme simplicity, we believe that this conjecture is a worthwhile direction for future theoretical work.

Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we explicitly impose the maximum entropy principle. We also propose the unified probabilistic framework, enabling solvers using standard techniques. Experiments on a variety of datasets bear out the notable benefit of our method in terms of adaptiveness and robustness.

As growing numbers of real world applications involve imbalanced class distribution or unequal costs for mis- classification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of class distribution on 14 imbalanced data-sets by using statistical and graphical methods to address the important issue of understanding the effect of vary- ing levels of class distribution on bagging predictors. The experimental results demonstrate that bagging NB and MLP are insensitive to various levels of imbalanced class distribution.

Illegal extraction of forest resources is fought, in many developing countries, by patrols that try to make this activity less profitable, using the threat of confiscation. With a limited budget, officials will try to distribute the patrols throughout the forest intelligently, in order to most effectively limit extraction. Prior work in forest economics has formalized this as a Stackelberg game, one very different in character from the discrete Stackelberg problem settings previously studied in the multiagent literature. Specifically, the leader wishes to minimize the distance by which a profit-maximizing extractor will trespass into the forest---or to maximize the radius of the remaining ``pristine'' forest area. The follower's cost-benefit analysis of potential trespass distances is affected by the likelihood of being caught and suffering confiscation. In this paper, we give a near-optimal patrol allocation algorithm and a 1/2-approximation algorithm, the latter of which is more efficient and yields simpler, more practical patrol allocations. Our simulations indicate that these algorithms substantially outperform existing heuristic allocations.