Understanding ecological complexity has stymied scientists for decades. Recent elucidation of the famously coined "devious strategies for stability in enduring natural systems" has opened up a new field of computational analyses of complex ecological networks where the nonlinear dynamics of many interacting species can be more realistically modeled and understood. Here, we describe the first extension of this field to include coupled human-natural systems. This extension elucidates new strategies for sustaining extraction of biomass (e.g., fish, forests, fiber) from ecosystems that account for ecological complexity and can pursue multiple goals such as maximizing economic profit, employment and carbon sequestration by ecosystems. Our more realistic modeling of ecosystems helps explain why simpler "maximum sustainable yield" bioeconomic models underpinning much natural resource extraction policy leads to less profit, biomass, and biodiversity than predicted by those simple models. Current research directions of this integrated natural and social science include applying artificial intelligence, cloud computing, and multiplayer online games.

In this paper, we address the problem of automatically generating human-like descriptions for unseen images, given a collection of images and their corresponding human-generated descriptions. Previous attempts for this task mostly rely on visual clues and corpus statistics, but do not take much advantage of the semantic information inherent in the available image descriptions. Here, we present a generic method which benefits from all these three sources (i.e. visual clues, corpus statistics and available descriptions) simultaneously, and is capable of constructing novel descriptions. Our approach works on syntactically and linguistically motivated phrases extracted from the human descriptions. Experimental evaluations demonstrate that our formulation mostly generates lucid and semantically correct descriptions, and significantly outperforms the previous methods on automatic evaluation metrics. One of the significant advantages of our approach is that we can generate multiple interesting descriptions for an image. Unlike any previous work, we also test the applicability of our method on a large dataset containing complex images with rich descriptions.

We consider the problem of predicting winners in elections given complete knowledge about all possible candidates, all possible voters (together with their preferences), but in the case where it is uncertain either which candidates exactly register for the election or which voters cast their votes. Under reasonable assumptions our problems reduce to counting variants of election control problems. We either give polynomial-time algorithms or prove #P-completeness results for counting variants of control by adding/deleting candidates/voters for Plurality, k -Approval, Approval, Condorcet, and Maximin voting rules.

The paper identifies a special case in which the complex problem of synthesis from specifications in temporal-epistemic logic can be reduced to the simpler problem of model checking such specifications. An application is given of strategy synthesis in pursuit-evasion games, where one or more pursuers with incomplete information aim to discover theexistence of an evader. Experimental results are provided to evaluate the feasibility of the approach.

We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, evaluates against the latent utility at the corresponding level and moves up until a suitable ordinal level is found. Crucial to this generative process is the underlying utility random variables that govern the generation of ratings and their modelling choices. To this end, we make a novel use of the generalised extreme value distributions, which is found to be particularly suitable for our modeling tasks and at the same time, facilitate our inference and learning procedure. The proposed approach is flexible to incorporate features from both the user and the item. We evaluate the proposed framework on three well-known datasets: MovieLens, Dating Agency and Netflix. In all cases, it is demonstrated that the proposed work is competitive against state-of-the-art collaborative filtering methods.

An interesting strategy called configuration checking (CC) was recently proposed to handle the cycling problem in local search for Minimum Vertex Cover. A natural question is whether this CC strategy also works for SAT. The direct application of CC did not result in stochastic local search (SLS) algorithms that can compete with the current best SLS algorithms for SAT. In this paper, we propose a new heuristic based on CC for SLS algorithms for SAT, which is called configuration checking with aspiration (CCA). It is used to develop a new SLS algorithm called Swcca. The experiments on random 3-SAT instances show that Swcca significantly outperforms Sparrow2011, the winner of the random satisfiable category of the SAT Competition 2011, which is considered to be the best local search solver for random 3-SAT instances. Moreover, the experiments on structured instances show that Swcca is competitive with Sattime, the best local search solver for the crafted benchmark in the SAT Competition 2011.

Details of our work can be found in the technical report, which is available at http://gqi.limewebs.com/aaaist12.pdf. In this paper, we propose a new operator for revising ABoxes in DL-Lite ontologies.

A probabilistic variant of ATL* logic is proposed to work with multi-player games of incomplete information and synchronous perfect recall. The semantics of the logic is settled over probabilistic interpreted system and partially observed probabilistic concurrent game structure. While unexpectedly, the model checking problem is in general undecidable even for single-group fragment, we find a fragment whose complexity is in 2-EXPTIME. The usefulness of this fragment is shown over a land search scenario.

In recent years, several systems have been proposed that learn the rules of a simple card or board game solely from visual demonstration. These systems were constructed for specific games and rely on substantial background knowledge. We introduce a general system for learning board game rules from videos and demonstrate it on several well-known games. The presented algorithm requires only a few demonstrations and minimal background knowledge, and, having learned the rules, automatically derives position evaluation functions and can play the learned games competitively. Our main technique is based on descriptive complexity, i.e. the logical means necessary to define a set of interest. We compute formulas defining allowed moves and final positions in a game in different logics and select the most adequate ones. We show that this method is well-suited for board games and there is strong theoretical evidence that it will generalize to other problems.

We study time-consistency of optimization problems, where we say that an optimization problem is time-consistent if its optimal solution, or the optimal policy for choosing actions, does not depend on when the optimization problem is solved. Time-consistency is a minimal requirement on an optimization problem for the decisions made based on its solution to be rational. We show that the return that we can gain by taking "optimal" actions selected by solving a time-inconsistent optimization problem can be surely dominated by that we could gain by taking "suboptimal" actions. We establish sufficient conditions on the objective function and on the constraints for an optimization problem to be time-consistent. We also show when the sufficient conditions are necessary. Our results are relevant in stochastic settings particularly when the objective function is a risk measure other than expectation or when there is a constraint on a risk measure.