In the context of question-answering systems, there are several strategies for scoring candidate answers to definition queries including centroid vectors, bi-term and context language models. These techniques use only positive examples (i.e., descriptions) when building their models. In this work, a maximum entropy based extension is proposed for context language models so as to account for regularities across non-descriptions mined from web-snippets. Experiments show that this extension outperforms other strategies increasing the precision of the top five ranked answers by more than 5%. Results suggest that web-snippets are a cost-efficient source of non-descriptions, and that some relationships extracted from dependency trees are effective to mine for candidate answer sentences.

Freuder and Elfe (1996) introduced Neighborhood Inverse Consistency (NIC) for binary CSPs. In this paper, we introduce RNIC, the extension of NIC to non-binary CSPs, and describe a practical algorithm for enforcing it. We propose an adaptive strategy to weaken or strengthen this property based on the connectivity of the network. We demonstrate the effectiveness of RNIC as a full lookahead strategy during search for solving difficult benchmark problems.

Many networks are complex dynamical systems, where both attributes of nodes and topology of the network (link structure) can change with time. We propose a model of co-evolving networks where both node attributes and network structure evolve under mutual influence. Specifically, we consider a mixed membership stochastic blockmodel, where the probability of observing a link between two nodes depends on their current membership vectors, while those membership vectors themselves evolve in the presence of a link between the nodes. Thus, the network is shaped by the interaction of stochastic processes describing the nodes, while the processes themselves are influenced by the changing network structure. We derive an efficient variational inference procedure for our model, and validate the model on both synthetic and real-world data.

One of the most ubiquitous AI applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, time-varying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traffic. We have applied our algorithm to large-scale traffic route planning, and demonstrated that our self-aware route planner can more accurately predict future traffic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm.

Their information needs are long and popular information needs of the masses. Topic term and highly dynamic - nearly everything about this topic specificity, customizability, and automatically pursuing the is in flux. For these users, information search can be made long term unique information needs of individual users are more effective with knowledge about the field and about the not among the strengths of current main stream search engines types of documents being retrieved. Because the resource (Jansen, Spink, and Saracevic 2000) (Teevan, Dumais, management decisions require judgment about the materials and Horvitz 2005). This gap has inspired web personalization collected, the users require confidentiality and must trust the and collaborative information seeking tools such as sources. Google Alerts and has encouraged topic-specific blogs and Matilda is designed to 1) tailor information collection for podcasts.

The study of phase transition phenomenon of NP complete problems plays an important role in understanding the nature of hard problems. In this paper, we follow this line of research by considering the problem of counting solutions of Constraint Satisfaction Problems (#CSP). We consider the random model, i.e. RB model. We prove that phase transition of #CSP does exist as the number of variables approaches infinity and the critical values where phase transitions occur are precisely located. Preliminary experimental results also show that the critical point coincides with the theoretical derivation. Moreover, we propose an approximate algorithm to estimate the expectation value of the solutions number of a given CSP instance of RB model.

Symbolic search using BDDs usually saves huge amounts of memory, while in some domains its savings are moderate at best. It is an open problem to determine if BDDs work well for a certain domain. Motivated by finding evidences for BDD growths for state space search, in this paper we are concerned with symbolic search in the domain of Connect Four. We prove that there is a variable ordering for which the set of all possible states – when continuing after a terminal state has been reached – can be represented by polynomial sized BDDs, whereas the termination criterion leads to an exponential number of nodes in the BDD given any variable ordering.

We consider the problem of routing electric vehicles (EV) in the most energy-efficient way within a road network taking into account both their limited energy supply as well as their ability to recuperate energy. Employing a classical result by Johnson and an observation about Dijkstra under non-constant edge costs we obtain O(n log n +m) query time after a O(nm) preprocessing phase for any road network graph whose edge costs represent energy consumption or recuperation.If the energy recuperation is height induced in a very natural way,the preprocessing phase can even be omitted. We then adapt a technique for speeding-up (unconstrained) shortest path queries to our scenario to achieve a speed-up of another factor of around 20. Our results drastically improve upon the recent results in (Artmeier et al. 2010) and allow for route planning of EVs in an instant even on large networks.

Automated feature discovery is a fundamental problem in machine learning. Although classical feature discovery methods do not guarantee optimal solutions in general, it has been recently noted that certain subspace learning and sparse coding problems can be solved efficiently, provided the number of features is not restricted a priori. We provide an extended characterization of this optimality result and describe the nature of the solutions under an expanded set of practical contexts. In particular, we apply the framework to a semi-supervised learning problem, and demonstrate that feature discovery can co-occur with input reconstruction and supervised training while still admitting globally optimal solutions. A comparison to existing semi-supervised feature discovery methods shows improved generalization and efficiency.

When mobile robots perform tasks in environments with humans, it seems appropriate for the robots to rely on such humans for help instead of dedicated human oracles or supervisors. However, these humans are not always available nor always accurate. In this work, we consider human help to a robot as concretely providing observations about the robot's state to reduce state uncertainty as it executes its policy autonomously. We model the probability of receiving an observation from a human in terms of their availability and accuracy by introducing Human Observation Providers POMDPs (HOP-POMDPs). We contribute an algorithm to learn human availability and accuracy online while the robot is executing its current task policy. We demonstrate that our algorithmis effective in approximating the true availability and accuracy of humans without depending on oracles to learn, thus increasing the tractability of deploying a robot that can occasionally ask for help.