In 1990, Thomas Schwartz proposed the conjecture that every nonempty tournament has a unique minimal TEQ-retentive set (TEQ stands for tournament equilibrium set). A weak variant of Schwartz's Conjecture was recently proposed by Felix Brandt. However, both conjectures were disproved very recently by two counterexamples. In this paper, we prove sufficient conditions for infinite classes of tournaments that satisfy Schwartz's Conjecture and Brandt's Conjecture. Moreover, we prove that TEQ can be calculated in polynomial time in several infinite classes of tournaments. Furthermore, our results reveal some structures that are forbidden in every counterexample to Schwartz's Conjecture.

Nowadays, multiagent planning under uncertainty scales to tens or even hundreds of agents. However, current methods either are restricted to problems with factored value functions, or provide solutions without any guarantees on quality. Methods in the former category typically build on heuristic search using upper bounds on the value function. Unfortunately, no techniques exist to compute such upper bounds for problems with non-factored value functions, which would additionally allow for meaningful benchmarking of methods of the latter category. To mitigate this problem, this paper introduces a family of influence-optimistic upper bounds for factored Dec-POMDPs without factored value functions. We demonstrate how we can achieve firm quality guarantees for problems with hundreds of agents.

Agents can be thought of as following a social behaviour, depending on the context in which they are interacting. We devise a computationally grounded  mechanism to represent and reason about others in social terms, reflecting the local perspective of an agent (first-person view), to support both stereotypical and empathetic reasoning. We use a hierarchy of agent models to discriminate which behaviours of others are plausible, and decide which behaviour for ourselves is socially acceptable, i.e. conforms to the social context. To this aim, we investigate the implications of considering agents capable of various degrees of theory of mind, and discuss a scenario showing how this affects behaviour.

Model-Based Diagnosis (MBD) finds a growing number of uses in different settings, which include software fault localization, debugging of spreadsheets, web services, and hardware designs, but also the analysis of biological systems, among many others. Motivated by these different uses, there have been significant improvements made to MBD algorithms in recent years. Nevertheless, the analysis of larger and more complex systems motivates further improvements to existing approaches. This paper proposes a novel encoding of MBD into maximum satisfiability (MaxSAT). The new encoding builds on recent work on using Propositional Satisfiability (SAT) for MBD, but identifies a number of key optimizations that are very effective in practice. The paper also proposes a new set of challenging MBD instances, which can be used for evaluating new MBD approaches. Experimental results obtained on existing and on the new MBD problem instances, show conclusive performance gains over the current state of the art.

Transitive distance is an ultrametric with elegant properties for clustering. Conventional transitive distance can be found by referring to the minimum spanning tree (MST). We show that such distance metric can be generalized onto a minimum spanning random forest (MSRF) with element-wise max pooling over the set of transitive distance matrices from an MSRF. Our proposed approach is both intuitively reasonable and theoretically attractive. Intuitively, max pooling alleviates undesired short links with single MST when noise is present. Theoretically, one can see that the distance metric obtained max pooling is still an ultrametric, rendering many good clustering properties. Comprehensive experiments on data clustering and image segmentation show that MSRF with max pooling improves the clustering performance over single MST and achieves state of the art performance on the Berkeley Segmentation Dataset.

However, humans are proved to question: What is the connection between the arguments proposed behave differently, mixing rational and emotional by the participants of a debate and their emotional attitudes to guide their actions, and it has been status? Such question breaks down into the following subquestions: claimed that there exists a strong connection between (1) is the polarity of arguments and the relations the argumentation process and the emotions among them correlated with the polarity of the detected emotions?, felt by people involved in such process. In this paper, and (2) what is the relation between the kind and the we assess this claim by means of an experiment: amount of arguments proposed in a debate, and the mental during several debates people's argumentation engagement detected among the participants of the debate? in plain English is connected and compared to the emotions automatically detected from the participants. To answer these questions, we propose an empirical evaluation Our results show a correspondence between of the connection between argumentation and emotions.

Stackelberg game models of security have received much attention, with a number of approaches for computing Stackelberg equilibria in games with a single defender protecting a collection of targets. In contrast, multi-defender security games have received significantly less attention, particularly when each defender protects more than a single target. We fill this gap by considering a multidefender security game, with a focus on theoretical characterizations of equilibria and the price of anarchy. We present the analysis of three models of increasing generality, two in which each defender protects multiple targets. In all models, we find that the defenders often have the incentive to overprotect the targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Surprisingly, when we consider a more general model, this results obtains only in a “corner” case in the space of parameters; in most cases, however, the price of anarchy converges to a constant when the number of defenders increases.

Real world networks typically exhibit non uniform edge densities with there being a higher concentration of edges within modules or communities. Various scoring functions have been proposed to quantify the quality of such communities. In this paper, we argue that the popular scoring functions suffer from certain limitations. We identify the necessary features that a scoring function should incorporate in order to characterize good community structure and propose a new scoring function, CEIL (Community detection using External and Internal scores in Large networks), which conforms closely with our characterization. We also demonstrate experimentally the superiority of our scoring function over the existing scoring functions. Modularity, a very popular scoring function, exhibits resolution limit, i.e., one cannot find communities that are much smaller in size compared to the size of the network. In many real world networks, community size does not grow in proportion to the network size. This implies that resolution limit is a serious problem in large networks. Modularity is still very popular since it offers many advantages such as fast algorithms for maximizing the score, and non-trivial community structures corresponding to the maxima. We show analytically that the CEIL score does not suffer from resolution limit. We also modify the Louvain method, one of the fastest greedy algorithms for maximizing modularity, to maximize the CEIL score. We show that our algorithm gives the expected communities in synthetic networks as opposed to maximizing modularity. We also show that the community labels given by our algorithm matches closely with the ground truth community labels in real world networks. Our algorithm is on par with Louvain method in computation time and hence scales well to large networks.

The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework. The theory is based on a generalisation of Dung’s notion of acceptability, making it sensitive to the numbers of attacks and counter-attacks on arguments. Graded generalisations of argumentation semantics are then obtained and studied. The theory is applied by showing how it can arbitrate between competing preferred extensions and how it captures a specific form of accrual in instantiated argumentation.

Unfair rating attacks happen in existing trust and reputation systems, lowering the quality of the systems. There exists a formal model that measures the maximum impact of independent attackers [Wang et al., 2015] — based on information theory. We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack). Using (1), we identify the strongest collusion attacks, helping construct robust trust system. Using (2), we identify the strength of (classes of) attacks that we found in the literature. Based on this, we help to overcome a shortcoming of current research into collusion-resistance — specific (types of) attacks are used in simulations, disallowing direct comparisons between analyses of systems.