However, generation of MAS models that SFL is a well-suited technique for MASs. is both error-prone and time intense, as it exponentially Literature has shown that there is no standard similarity increases with the number of agents coefficient that yields the best result for SFL [Yoo et al., 2014; and their interactions. In this paper, we propose Hofer et al., 2015; Le et al., 2013]. Empirical evaluation is a lightweight, automatic debugging-based technique, therefore essential to establish which set of heuristics excels coined ESFL-MAS, which shortens the diagnostic for the specific context to which SFL is being applied. To the process, while only relying on minimal best of our knowledge, SFL has not as yet been applied to information about the system. ESFL-MAS uses a diagnose behavioural faults in MASs; there is hence the need heuristic that quantifies the suspiciousness of an to empirically evaluate different formulae using known faults agent to be faulty; therefore, different heuristics to compare the performance yielded by several coefficients.

We study resource allocation in a model due to Brams and King [2005] and further developed by Baumeister et al. [2014]. Resource allocation deals with the distribution of resources to agents. We assume resources to be indivisible, nonshareable, and of single-unit type. Agents have ordinal preferences over single resources. Using scoring vectors, every ordinal preference induces a utility function. These utility functions are used in conjunction with utilitarian social welfare to assess the quality of allocations of resources to agents. Then allocation correspondences determine the optimal allocations that maximize utilitarian social welfare. Since agents may have an incentive to misreport their true preferences, the question of strategy-proofness is important to resource allocation. We assume that a manipulator has a strictly monotonic and strictly separable linear order on the power set of the resources. We use extension principles (from social choice theory, such as the Kelly and the Gärdenfors extension) for preferences to study manipulation of allocation correspondences. We characterize strategy-proofness of the utilitarian allocation correspondence: It is Gärdenfors/Kelly-strategy-proof if and only if the number of different values in the scoring vector is at most two or the number of occurrences of the greatest value in the scoring vector is larger than half the number of goods.

The probabilistic serial (PS) rule is a prominent randomized rule for assigning indivisible goods to agents. Although it is well known for its good fairness and welfare properties, it is not strategyproof. In view of this, we address several fundamental questions regarding equilibria under PS. Firstly, we show that Nash deviations under the PS rule can cycle. Despite the possibilities of cycles, we prove that a pure Nash equilibrium is guaranteed to exist under the PS rule. We then show that verifying whether a given profile is a pure Nash equilibrium is coNP-complete, and computing a pure Nash equilibrium is NP-hard. For two agents, we present a linear-time algorithm to compute a pure Nash equilibrium which yields the same assignment as the truthful profile. Finally, we conduct experiments to evaluate the quality of the equilibria that exist under the PS rule, finding that the vast majority of pure Nash equilibria yield social welfare that is at least that of the truthful profile.

We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.

We contribute a novel approach to understand, dialogue, plan, and execute complex sentences to command a mobile service robot. We define a complex command as a natural language sentence consisting of sensing-based conditionals, conjunctions, and disjunctions. We introduce a flexible template-based algorithm to extract such structure from the parse tree of the sentence. As the complexity of the command increases, extracting the right structure using the template-based algorithm decreases becomes more problematic. We introduce two different dialogue approaches that enable the user to confirm or correct the extracted command structure. We present how the structure used to represent complex commands can be directly used for planning and execution by the service robot. We show results on a corpus of 100 complex commands

Computer-aided translation (CAT) system is the most popular tool which helps human translators perform language translation efficiently. To further improve the efficiency, there is an increasing interest in applying the machine translation (MT) technology to upgrade CAT. Post-editing is a standard approach: human translators generate the translation by correcting MT outputs. In this paper, we propose a novel approach deeply integrating MT into CAT systems: a well-designed input method which makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses and n-best translation lists. Our proposed approach allows human translators to focus on choosing better translation results with less time rather than just complete translation themselves. The extensive experiments demonstrate that our method saves more than 14% time and over 33% keystrokes, and it improves the translation quality as well by more than 3 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.

The recent landmark "right to be forgotten" ruling by the EU Court gives EU citizens the right to remove certain links that are "inaccurate, inadequate, irrelevant or excessive" from search results under their names. While we agree with the spirit of the ruling — to empower individuals to manage their personal data while keeping a balance between such right and the freedom of expression, we believe that the ruling is impractical as it provides neither precise criteria for evaluating removal requests nor concrete guidelines for implementation. Consequently, Google's current implementation has several problems concerning scalability, objectivity, and responsiveness. Instead of the right to be forgotten, we propose the right to obscure certain facts about oneself on search engines, and a simple mechanism which respects the spirit of the ruling by giving people more power to influence search results for queries on their names. Specifically, under our proposed mechanism, data subjects will be able to register minus terms, and search results for their name queries that contain such terms would be filtered out. We implement a proof-of-concept search engine following the proposed mechanism, and conduct experiments to explore the influences it might have on users' impressions on different data subjects.

In this paper we investigate the model-checking problem of pushdown multi-agent systems for ATL* specifications.To this aim, we introduce pushdown game structures over which ATL* formulas are interpreted. We show an algorithm that solves the addressed model-checking problem in 3ExpTime. We also provide a 2ExpSpace lower bound by showing a reduction from the word acceptance problem for deterministic Turing machines with doubly exponential space.

We investigate the general problem of establishing whether a swarm satisfies an emergent property. We put forward a formal model for swarms that accounts for their nature of unbounded collections of agents following simple local protocols. We formally define the decision problem of determining whether a swarm satisfies an emergent property. We introduce a sound and complete procedure for solving the problem. We illustrate the technique by applying it to the Beta aggregation algorithm.

RB+-ATL is an extension of ATL where it is possible to model consumption and production of several resources by a set of agents. The model-checking problem for RB+-ATL is known to be decidable. However the only available model-checking algorithm for RB+-ATL uses a forward search of the state space, and hence does not have an efficient symbolic implementation. In this paper, we consider a fragment of RB+-ATL, 1RB+-ATL, that allows only one resource type. We give a symbolic model-checking algorithm for this fragment of RB+-ATL, and evaluate the performance of an MCMAS-based implementation of the algorithm on an example problem that can be scaled to large state spaces.