Trust schemes can identify such nodes, as they Wireless sensor networks are being increasingly can predict a node's behavior (quality) both directly, via evaluation used for sustainable development. The task of routing based on its past actions, and indirectly, using recommendations in these resource-constraint networks is particularly (opinions) from other nodes. However, many challenging as they operate over prolonged trust schemes cannot effectively handle attacks targeting trust deployment periods, necessitating optimal use of systems themselves [Sun et al., 2006] i.e., they are heavily their resources. Moreover, due to the deployment affected by malicious nodes deliberately providing misleading in unattended environments, they become an easy opinions (unfair ratings) about other nodes.

Word problems are an established technique for teaching mathematical modeling skills in K-12 education. However, many students find word problems unconnected to their lives, artificial, and uninteresting. Most students find them much more difficult than the corresponding symbolic representations. To account for this phenomenon, an ideal pedagogy might involve an individually crafted progression of unique word problems that form a personalized plot. We propose a novel technique for automatic generation of personalized word problems. In our system, word problems are generated from general specifications using answer-set programming (ASP). The specifications include tutor requirements (properties of a mathematical model), and student requirements (personalization, characters, setting). Our system takes a logical encoding of the specification, synthesizes a word problem narrative and its mathematical model as a labeled logical plot graph, and realizes the problem in natural language. Human judges found our problems as solvable as the textbook problems, with a slightly more artificial language.

We introduce the study of adversarial effects on wisdom of the crowd phenomena. In particular, we examine the ability of an adversary to influence a social network so that the majority of nodes are convinced by a falsehood, using its power to influence a certain fraction, μ < 0.5 of N experts. Can a bad restaurant make a majority of the overall network believe in the quality of that restaurant by misleading a certain share of food critics into believing its food is good, and use the influence of those experts to make a majority of the overall network to believe in the quality of that restaurant? We are interested in providing an agent, which does not necessarily know the graph structure nor who the experts are, to determine the true value of a binary property using a simple majority. We prove bounds on the social graph's maximal degree, which ensure that with a high probability the adversary will fail (and the majority vote will coincide with the true value) when it can choose who the experts are, while each expert communicates the true value with probability p > 0.5. When we examine expander graphs as well as random graphs we prove such bounds even for stronger adversaries, who are able to pick and choose not only who the experts are, but also which ones of them would communicate the wrong values, as long as their proportion is 1-p. Furthermore, we study different propagation models and their effects on the feasibility of obtaining the true value for different adversary types.

The paper faces the problem of plan repair in presence of numeric information, by providing a new method for the intelligent selection of numeric macro actions. The method relies on a generalization of deordering, extended with new conditions accounting for dependencies and threats implied by the numeric components. The deordering is used as a means to infer (hopefully) minimal ordering constraints then used to extract independent and informative macro actions. Each macro aims at compactly representing a sub-solution for the overall planning problem. To verify the feasibility of the approach, the paper reports experiments in various domains from the International Planning Competition% measuring the performance of the new strategy using two state of the art numeric planning systems; i.e., Colin Metric-FF. Results show (i) the competitiveness of the strategy in terms of coverage, time and quality of the resulting plans wrt current approaches, and (ii) the actual independence from the planner employed.

Despite some success of Perfect Information Monte Carlo Sampling (PIMC) in imperfect information games in the past, it has been eclipsed by other approaches in recent years. Standard PIMC has well-known shortcomings in the accuracy of its decisions, but has the advantage of being simple, fast, robust and scalable, making it well-suited for imperfect information games with large state-spaces. We propose Presumed Value PIMC resolving the problem of overestimation of opponent's knowledge of hidden information in future game states. The resulting AI agent was tested against human experts in Schnapsen, a Central European 2-player trick-taking card game, and performs above human expert-level.

Managing inconsistency in DL-Lite knowledge bases where the assertional base is prioritized is a crucial problem in many applications. This is especially true when the assertions are provided by multiple sources having different reliability levels. This paper first reviews existing approaches for selecting preferred repairs. It then focuses on suitable strategies for handling inconsistency in DL-Lite knowledge bases. It proposes new approaches based on the selection of only one preferred repair. These strategies have as a starting point the so-called non-defeated repair and add one of the following principles: deductive closure, consistency, cardinality and priorities. Lastly, we provide a comparative analysis followed by an experimental evaluation of the studied approaches.

Conflict-Based Search (CBS) and its enhancements, Meta-Agent CBS and bypassing conflicts are amongst the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper introduces two new improvements to CBS and incorporates them into a coherent, improved version of CBS, namely ICBS. Experimental results show that each of these improvements further reduces the runtime over the existing CBS-based approaches. When all improvements are combined, an even larger improvement is achieved, producing state-of-the art results for a number of domains.

This paper deals with the problem of packing two-dimensional objects of quite arbitrary shapes including in particular curved shapes (like ellipses) and assemblies of them. This problem arises in industry for the packaging and transport of bulky objects which are not individually packed into boxes, like car spare parts. There has been considerable work on packing curved objects but, most of the time, with specific shapes; one famous example being the circle packing problem. There is much less algorithm for the general case where different shapes can be mixed together. A successful approach has been proposed recently in Martinez et al. (T. Martinez, L. Vitorino, F. Fages, and A. Aggoun. On Solving Mixed Shapes Packing Problems by Continuous Optimization with the CMA Evolution Strategy. In Proceedings of the first BRICS countries congress on Computational Intelligence, 2013) and the algorithm we propose here is an extension of their work. Martinez et al. use a stochastic optimization algorithm with a fitness function that gives a violation cost and equals zero when objects are all packed. Their main idea is to define this function as a sum of n!/(2!(n-2)!) elementary functions that measure the overlapping between each pair of different objects. However, these functions are ad-hoc formulas. Designing ad-hoc formulas for every possible combination of object shapes can be a very tedious task, which dramatically limits the applicability of their approach. The aim of this paper is to generalize the approach by replacing the ad-hoc formulas with a numerical algorithm that automatically measures the overlapping between two objects. Then, we come up with a fully black-box packing algorithm that accept any kind of objects.

Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X . It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates. This leads to a wider choice of potential IVs, but is rarely used yet. Here we address two issues with conditional IVs. First, they are conceptually rather distant to standard IVs; even variables that are independent of X could qualify as conditional IVs. We propose a new concept called ancestral IV , which interpolates between the two existing notions. Second, so far only exponential-time algorithms are known to find conditional IVs in a given causal diagram. Indeed, we prove that this problem is NP-hard. Nevertheless, we show that whenever a conditional IV exists, so does an ancestral IV, and ancestral IVs can be found in polynomial time. Together this implies a complete and constructive solution to causal effect identification using IVs in linear causal models.

The classical probabilistic entailment problem is to We apply the family of minimal violation measures from determine upper and lower bounds on the probability [Potyka, 2014] since they allow us to extend the classical notion of formulas, given a consistent set of probabilistic of models of a probabilistic knowledge base to inconsistent assertions. We generalize this problem ones. Intuitively, the generalized models are those probability by omitting the consistency assumption and, thus, functions that minimally violate the knowledge base provide a general framework for probabilistic reasoning [Potyka and Thimm, 2014]. We incorporate integrity constraints under inconsistency. To do so, we utilize and study a family of generalized entailment problems inconsistency measures to determine probability for probabilistic knowledge bases. More specifically, functions that are closest to satisfying the knowledge the contributions of this work are as follows: base. We illustrate our approach on several 1. We introduce the computational problem of generalized examples and show that it has both nice formal and entailment with integrity constraints in probabilistic logics computational properties.