The Web contains a large amount of information in the form of videos that remains inaccessible to the visually impaired people. We identify a class of videos whose information content can be approximately encoded as an audio, thereby increasing the amount of accessible videos. We propose a model to automatically identify such videos. Our model jointly relies on the textual metadata and visual content of the video. We use this model to re-rank Youtube video search results based on accessibility of the video. We present preliminary results by conducting a user study with visually impaired people to measure the effectiveness of our system.

Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.

Since Assumption-Based Argumentation (ABA) was introduced in the nineties,the structure and semantics of an ABA framework have been studied exclusively in logical termswithout any graphical representation.Here, we show how an ABA framework and its complete semantics can be displayed in a graph,clarifying the structure of the ABA framework as well as the resulting complete assumption labellings.Furthermore, we show that such an ABA graph can be used to represent the structureand semantics of a logic program (LP), based on the correspondence between the semantics of a LP and an ABA framework encoding this LP.

We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approach leverages bisimulation metrics, which have been used in prior work to generate features to represent the state space of MDPs.We address a drawback of this approach, which is the expensive computation of the bisimulation metrics. We propose an algorithm to generate an iteratively improving sequence of state space partitions. Partial metric computations guide the representation search and provide much lower space and computational complexity, while maintaining strong convergence properties. We provide theoretical results guaranteeing convergence as well as experimental illustrations of the accuracy and savings (in time and memory usage) of the new algorithm, compared to traditional bisimulation metric computation.

Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. To capture diversities among different domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF) model to improve the cross-domain recommendation performance. Experiments on several real world datasets demonstrate that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.

Numeric Timed Initial Fluents represent a new feature in PDDL that extends the concept of Timed Initial Literals to numeric fluents. They are particularly useful to model independent functions that change through time and influence the actions to be applied. Although they are very useful to model real world problems, they are not systematically defined in the family of PDDL languages and they are not implemented in any generic PDDL planner, except for POPF2 and UPMurphi. In this paper we present an extension of the planner POPF2 (POPF-TIF) to handle problems with numeric Timed Initial Fluents. We propose and evaluate two contributions: the first is based on improvements of the heuristic evaluation, while the second considers alternative search algorithms based on a mixture of Enforced Hill Climbing and Best First Search.

Grammatical Evolution (GE) is that area of genetic algorithms that evolves computer programs in high-level languages possessing a BNF grammar. In this work, we present GEF (“Grammatical Evolution for the Finch”), a system that employs grammatical evolution to create a Finch robot controller program in Java. The system uses both the traditional GE model as well as employing extensions and augmentations that push the boundaries of goal-oriented contexts in which robots typically act including a meta-level handler that fosters a level of self-awareness in the robot. To handle contingencies, the GEF system has been endowed with the ability to perform meta-level jumps. When confronted with unplanned events and dynamic changes in the environment, our robot will automatically transition to pursue another goal, changing fitness functions, and generate and invoke operating system level scripting to facilitate the change. The robot houses a raspberry pi controller that is capable of executing one (evolved) program while wirelessly receiving another over an asynchronous client. This work is part of an overall project that involves planning for contingencies. In this poster, we present the development framework and system architecture of GEF, including the newly discovered meta-level handler, as well as some other system successes, failures, and insights.

This paper briefly describes how the neural structure of fear conditioning has inspired to develop a computational intelligence model that is referred to as the brain emotional learning-inspired model (BELIM). The model is applied to predict long step ahead of solar activity and geomagnetic storms.

Intelligent systems that interact with humans typically require demonstrations and/or advice from the expert for optimal decision making. While the active learning formalism allows for these systems to incrementally acquire demonstrations from the human expert, most learning systems require all the advice about the domain in advance. We consider the problem of actively soliciting human advice in an inverse reinforcement learning setting where the utilities are learned from demonstrations. Our hypothesis is that such solicitation of advice reduces the burden on the human to provide advice about every scenario in advance.

Lazy Clause Generation (LCG) solvers dominate the current constraint programming competitions. These solvers successfully combine systematic propagation based search, global constraints and conflict clause learning from SAT solving into a hybrid approach. My research project extends the LCG methodology by using a mix of eager and lazy encodings and a richer set of constraint decompositions. Global Constraints exhibit a whole hierarchy of different decomposition into more basic constraints. In our work we want to take advantage of such hierarchies and identify criteria on how constraints could be decomposed before and during search.