This manuscript provides the research questions, proposed research plans, as well as expected contributions of my doctoral dissertation. My dissertation is primarily focused on providing computational approaches to study and analyze dialectical reasoning in large-scale online platforms. In particular, I aim to tackle the challenge of developing novel models to automatically classify explanation and argumentation as two different types of reasoning in text of discourse on the Web. The resulting models can be incorporated in the social Web environments to increase participants' awareness of others' reasoning types, which may lead to a more effective dialogue protocol and strategy.

As potential-based reward shaping functions (heuristic signals conflicts may exist between objectives, there is in general guiding exploration) (Brys et al. 2014a). We prove that this a need to identify (a set of) tradeoff solutions. The set modification preserves the total order, and thus also optimality, of optimal, i.e. non-dominated, incomparable solutions is of policies, mainly relying on the results by Ng, Harada, called the Pareto-front. We identify multi-objective problems and Russell (1999). This insight - that any MDP can be with correlated objectives (CMOP) as a specific subclass framed as a CMOMDP - significantly increases the importance of multi-objective problems, defined to contain those of this problem class, as well as techniques developed MOPs whose Pareto-front is so limited that one can barely for it, as these could be used to solve regular single-objective speak of tradeoffs (Brys et al. 2014b). By consequence, MDPs faster and better, provided several meaningful shapings the system designer does not care about which of the very can be devised.

Being able to quickly and naturally teach robots new knowledge is critical for many future open-world human-robot interaction scenarios. In this paper we present a novel approach to using natural language context for one-shot learning of visual objects, where the robot is immediately able to recognize the described object. We describe the architectural components and demonstrate the proposed approach on a robotic platform in a proof-of-concept evaluation.

Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem’s biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform many state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the better scores in F-measure and mean-square error tests.

Linear subspace is an important representation for many kinds of real-world data in computer vision and pattern recognition, e.g. faces, motion videos, speeches. In this paper, first we define pairwise angular similarity and angular distance for linear subspaces. The angular distance satisfies non-negativity, identity of indiscernibles, symmetry and triangle inequality, and thus it is a metric. Then we propose a method to compress linear subspaces into compact similarity-preserving binary signatures, between which the normalized Hamming distance is an unbiased estimator of the angular distance. We provide a lower bound on the length of the binary signatures which suffices to guarantee uniform distance-preservation within a set of subspaces. Experiments on face recognition demonstrate the effectiveness of the binary signature in terms of recognition accuracy, speed and storage requirement. The results show that, compared with the exact method, the approximation with the binary signatures achieves an order of magnitude speed-up, while requiring significantly smaller amount of storage space, yet it still accurately preserves the similarity, and achieves high recognition accuracy comparable to the exact method in face recognition.

Extracting the 3D geometry plays an important part in scene understanding. Recently, robust visual descriptors are proposed for extracting the indoor scene layout from a passive agent’s perspective, specifically from a single image. Their robustness is mainly due to modelling the physical interaction of the underlying room geometry with the objects and the humans present in the room. In this work we add the physical constraints coming from acoustic echoes, generated by an audio source, to this visual model. Our audio-visual 3D geometry descriptor improves over the state of the art in passive perception models as we show in our experiments.

The fundamental reasoning problem in RCC-8 is deciding In contrast to the synthetic RCC-8 networks that have the consistency of a set of constraints Θ, i.e., whether there been used in the literature for evaluating the aforementioned is a spatial configuration where the relations between the reasoners, the real-world networks of Table 1 are very sparse regions can be described by Θ. Traditionally in qualitative and one to two orders of magnitude larger. The labels on spatial reasoning (QSR) consistency of such sets is decided their edges contain 1 or 2 base RCC-8 relations forming a by a backtracking algorithm which optionally uses a pathconsistency disjunction. This kind of networks have not been employed algorithm as a preprocessing step for forward in any experimental evaluation of RCC-8 reasoners with the checking. In general, this problem is NPcomplete (Renz exception of (Sioutis and Koubarakis 2012) in which the network and Nebel 1999). However it has been shown in (Renz 1999) adm1 has been used. Typically, the literature focuses that there are tractable subsets of RCC-8 for which the consistency on quite smaller networks (20 to 1000 nodes) with an average problem can be decided by path-consistency. of 4 base RCC-8 relations per edge, and an average Table 1 depicts the characteristics of some real-world node degree ranging from 4 to 20. Deciding the consistency RCC-8 networks recording the topological relations between of real-world networks is a very important task. Inconsistencies administrative regions in Europe (networks nuts, might arise because their RCC-8 relations are computed adm1, and adm2) and the world (networks gadm1 and based on the geometries of geographical objects which gadm2), and the performance of the following reasoners often have not been captured correctly (e.g., overlapping geometries regarding consistency checking: Renz-Nebel01 (Renz and between two regions that in principle are externally Nebel 2001), GQR-1500 (Gantner, Westphal, and Woelfl connected). This is the case for the networks gadm1 and 2008; Westphal and Hué 2012), PPyRCC8 (Sioutis and gadm2.

Constraints over variable sequences are ubiquitous and many of their propagators have been inspired by dynamic programming (DP). We propose a conceptual framework for designing such propagators: pruning rules, in a functional notation, are refined upon the application of transformation operators to a DP-style formulation of a constraint; a representation of the (tuple) variable domains is picked; and a control of the pruning rules is picked.

Stochastic local search (SLS) algorithms have shown effectiveness on satisfiable instances of the Boolean satisfiability (SAT) problem. However, their performance is still unsatisfactory on random k-SAT at the phase transition, which is of significance and is one of the empirically hardest distributions of SAT instances. In this paper, we propose a new heuristic called DCCA, which combines two configuration checking (CC) strategies with different definitions of configuration in a novel way. We use the DCCA heuristic to design an efficient SLS solver for SAT dubbed DCCASat. The experiments show that the DCCASat solver significantly outperforms a number of state-of-the-art solvers on extensive random k-SAT benchmarks at the phase transition. Moreover, DCCASat shows good performance on structured benchmarks, and a combination of DCCASat with a complete solver achieves state-of-the-art performance on structured benchmarks.

Chavira and Darwiche 2008; Apsel and Brafman 2012)) and forms of planning (see e.g., (Palacios et al. 2005; It and more importantly the variable elimination rule (replacing proves useful when the problem under consideration (e.g., in the input CNF formula all the clauses containing a the satisfiability issue) can be solved more efficiently when given variable x by the set of all their resolvents over x) or the input formula has been first preprocessed (of course, the blocked clause elimination rule (removing every clause the preprocessing time is taken into account in the global containing a literal such that every resolvent obtained by resolving solving time). Some preprocessing techniques are nowadays on it is a valid clause).