This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some active learning/sensing and Bayesian optimization criteria and offer practitioners some flexibility to specify their desired choices for defining new tasks/problems. In particular, it utilizes a principled Bayesian sequential decision problem framework for jointly and naturally optimizing the exploration-exploitation trade-off. In general, the resulting induced GPP policy cannot be derived exactly due to an uncountable set of candidate observations. A key contribution of our work here thus lies in exploiting the Lipschitz continuity of the reward functions to solve for a nonmyopic adaptive epsilon-optimal GPP (epsilon-GPP) policy. To plan in real time, we further propose an asymptotically optimal, branch-and-bound anytime variant of epsilon-GPP with performance guarantee. We empirically demonstrate the effectiveness of our epsilon-GPP policy and its anytime variant in Bayesian optimization and an energy harvesting task.

We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com.

With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained.

Reasoning about temporal knowledge is a fundamental task in the area of artificial intelligence and knowledge representation. A key problem in this area is model checking, and indispensable for the state-of-the-art in solving this problem in large-scale settings is the technique of bounded model checking. We investigate the theoretical possibilities of this technique using parameterized complexity theory. In particular, we provide a complete parameterized complexity classification for the model checking problem for symbolically represented Kripke structures for various fragments of the temporal logics LTL, CTL and CTL*. We argue that a known result from the literature for a restricted fragment of LTL can be seen as an fpt-reduction to SAT, and show that such reductions are not possible for any of the other fragments of the temporal logics that we consider. As a by-product of our investigation, we develop a novel parameterized complexity class that can be seen as a parameterized variant of the Polynomial Hierarchy.

The Augmented Lagragian Method (ALM) and Alternating Direction Method of Multiplier (ADMM) have been powerful optimization methods for general convex programming subject to linear constraint. We consider the convex problem whose objective consists of a smooth part and a nonsmooth but simple part. We propose the Fast Proximal Augmented Lagragian Method (Fast PALM) which achieves the convergence rate O(1/K2), compared with O(1/K) by the traditional PALM. In order to further reduce the per-iteration complexity and handle the multi-blocks problem, we propose the Fast Proximal ADMM with Parallel Splitting (Fast PL-ADMM-PS) method. It also partially improves the rate related to the smooth part of the objective function. Experimental results on both synthesized and real world data demonstrate that our fast methods significantly improve the previous PALM and ADMM

We present a novel approach to account for preferences in a well known structured argumentation formalism, Assumption-Based Argumentation (ABA). The new formalism, called ABA+, incorporates object-level preferences (over assumptions) directly into the attack relation to reverse attacks. We give several basic desirable properties of ABA+.

We investigate a higher-order extension of the description logic (DL) SROIQ that provides a fixedly interpreted role semantically coupled with instantiation. It is useful to express interesting meta-level constraints on the modelled ontology. We provide a model-theoretic characterization of the semantics, and we show the decidability by means of reduction.

Methods for learning word representations using large text corpora have received much attention lately due to their impressive performancein numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection.Despite their success, these data-driven word representation learning methods do not considerthe rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNetthat represent the meanings of words by defining the various relationships that exist among the words in a language.We consider the question, can we improve the word representations learnt using a corpora by integrating theknowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy.

We propose a declarative framework for representing and reasoning about truthfulness of agents using answer set programming. We show how statements by agents can be evaluated against a set of observations over time equipped with our knowledge about the actions of the agents and the normal behavior of agents. We illustrate the framework using examples and discuss possible extensions that need to be considered.

Solving a multi-objective constraint optimization problem (MO-COP) typically consists in computing all Pareto optimal solutions, which are exponentially many in the general case. This causes two problems: time complexity and lack of decisiveness. We present an approach which, given a number k of desired solutions, selects k Pareto optimal solutions that are representative of the Pareto front. We analyze the computational complexity of the underlying computational problem and provide exact and approximation procedures.