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 margin of victory of an election is a useful measure to capture the robustness of an election outcome. It also plays a crucial role in determining the sample size of various algorithms in post election audit, polling etc. In this work, we present efficient sampling based algorithms for estimating the margin of victory of elections. More formally, we introduce the (c, ε, δ)–MARGIN OF VICTORY problem, where given an election E on n voters, the goal is to estimate the margin of victory M(E) of E within an additive factor of cM(E)+εn. We study the (c, ε, δ)–MARGIN OF VICTORY problem for many commonly used voting rules including scoring rules, approval, Bucklin, maximin, and Copelandα. We observe that even for the voting rules for which computing the margin of victory is NP-Hard, there may exist efficient sampling based algorithms, as observed in the cases of maximin and Copelandα voting rules.

In this work, we investigate the problem of large-scale mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually pick tasks to perform, we automatically make task recommendation based on workers' historical trajectories and desired time budgets. The challenge of predicting workers' trajectories is that it is faced with uncertainties, as a worker does not take same routes every day. In this work, we depart from deterministic modeling and study the stochastic task recommendation problem where each worker is associated with several predicted routine routes with probabilities. We formulate this problem as a stochastic integer linear program whose goal is to maximize the expected total utility achieved by all workers. We further exploit the separable structures of the formulation and apply the Lagrangian relaxation technique to scale up computation. Experiments have been performed over the instances generated using the real Singapore transportation network. The results show that we can find significantly better solutions than the deterministic formulation.

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.

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.

We develop a methodology to model and verify Regarding the second limitation, proposals have been put open multi-agent systems (OMAS), where agents forward to consider a set of objects that vary at design time; may join in or leave at run time. Further, we specify the set of agents is normally considered to be finite in each properties of interest on OMAS in a variant of firstorder system run. This is a sensible assumption in many scenarios, temporal-epistemic logic, whose characterising but there are applications of MAS (e.g., e-commerce, smart features include epistemic modalities indexed grids) where an unbounded number of agents may freely enter to individual terms, interpreted on agents appearing and leave the system at run time. There is, therefore, at a given state. This formalism notably allows a need to account for the unbounded and possibly infinite to express group knowledge dynamically. We study agents joining in or leaving an open MAS. In this setting it the verification problem of these systems and show is still of interest to reason about their evolution and what that, under specific conditions, finite bisimilar abstractions they know individually and collectively. For example, in an can be obtained.

Computing the set of optimal solutions for a multi-objective constraint optimisation problem can be computationally very challenging. Also, when solutions are only partially ordered, there can be a number of different natural notions of optimality, one of the most important being the notion of Possibly Optimal, i.e., optimal in at least one scenario compatible with the inter-objective tradeoffs. We develop an AND/OR Branch-and-Bound algorithm for computing the set of Possibly Optimal solutions, and compare variants of the algorithm experimentally.