We consider the design and enactment of multiagent protocols that describe collaboration using "normative" or "social" abstractions, specifically, commitments. A (multiagent) protocol defines the relevant social states and how they progress; each participant maintains a local projection of these states and acts accordingly. Protocols expose two important challenges: (1) how to compose them in a way that respects commitments and (2) how to verify the compliance of the parties with the social states. Individually, these challenges are inadequately studied and together not at all. We motivate the notion of a social context to capture how a protocol may be enacted. A protocol can be verifiably enacted when its participants can determine each other's compliance. We first show the negative result that even when protocols can be verifiably enacted in respective social contexts, their composition cannot be verifiably enacted in the composition of those social contexts. We next show how to expand such a protocol so that it can be verifiably enacted. Our approach involves design rules to specify composite protocols so they would be verifiably enactable. Our approach demonstrates a use of dialectical commitments, which have previously been overlooked in the protocols literature.

Materialisation precomputes all consequences of a set of facts and a datalog program so that queries can be evaluated directly (i.e., independently from the program). Rewriting optimises materialisation for datalog programs with equality by replacing all equal constants with a single representative; and incremental maintenance algorithms can efficiently update a materialisation for small changes in the input facts. Both techniques are critical to practical applicability of datalog systems; however, we are unaware of an approach that combines rewriting and incremental maintenance. In this paper we present the first such combination, and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation.

The Gibbard-Satterthwaite theorem implies the ubiquity of manipulators — voters who could change the election outcome in their favor by unilaterally modifying their vote. In this paper, we ask what happens if a given profile admits several such voters. We model strategic interactions among Gibbard–Satterthwaite manipulators as a normal-form game. We classify the 2-by-2 games that can arise in this setting for two simple voting rules, namely Plurality and Borda, and study the complexity of determining whether a given manipulative vote weakly dominates truth-telling, as well as existence of Nash equilibria.

Therefore, we have to rely on an AI algorithm Many collective human activities have been shown to fill in the blank spots. More precisely, we provide a relational to exhibit universal patterns. However, the possibility view on Label Propagation (LP) [Zhu et al., 2003; of regularities underlying researcher migration Bengio et al., 2006] and introduce a novel way to significantly in computer science (CS) has barely been explored speed it up based on equitable partitions. We call the resulting at global scale. To a large extend, this is due algorithm Compressed Label Propagation (CLP) because to official and commercial records being restricted, the original LPgraph is "lifted" or rather "compressed" before incompatible between countries, and especially not running vanilla LP on the smaller graph. Running CLP registered across researchers. We overcome these results in the first translational dataset for more than a million limitations by building our own, transnational, computer scientists on which we then learn statistical migration large-scale dataset inferred from publicly available models explaining the results in sociologically plausible information on the Web. Essentially, we use Label ways. To verify the quality of our inferred geo-tags and Propagation (LP) to infer missing geo-tags of statistical models, we additionally run CLP on an orders-ofmagnitude author-paper-pairs retrieved from online bibliographies.

This paper reports recent progress on modeling the grounded co-acquisition of syntax and semantics of locative spatial language in developmental robots. Weshow how a learner robot can learn to produce and interpret spatial utterances in guided-learning interactions with a tutor robot (equipped with a system for producing English spatial phrases). The tutor guides the learning process by simplifying the challenges and complexity of utterances, givesfeedback, and gradually increases the complexity of the language to be learnt. Our experiments show promising results towards long-term, incremental acquisition of natural language in a process of co-development of syntax and semantics.

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.

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.

We study voting games on binary issues, where voters might hold an objective over some issues at stake, while willing to strike deals on the remaining ones, and can influence one another’s voting decision before the vote takes place. We analyse voters’ rational behaviour in the resulting two-phase game, showing under what conditions undesirable equilibria can be removed as an effect of the pre-vote phase.

Active learning reduces the labeling cost by selectively querying the most valuable information from the annotator. It is essentially important for multi-label learning, where the labeling cost is rather high because each object may be associated with multiple labels. Existing multi-label active learning (MLAL) research mainly focuses on the task of selecting instances to be queried. In this paper, we disclose for the first time that the query type, which decides what information to query for the selected instance, is more important. Based on this observation, we propose a novel MLAL framework to query the relevance ordering of label pairs, which gets richer information from each query and requires less expertise of the annotator. By incorporating a simple selection strategy and a label ranking model into our framework, the proposed approach can reduce the labeling effort of annotators significantly. Experiments on 20 benchmark datasets and a manually labeled real data validate that our approach not only achieves superior performance on classification, but also provides accurate ranking for relevant labels.