We study a general class of multiagent optimization problems, together with a compact representation language of utilities based on weighted propositional formulas. We seek solutions maximizing utilitarian social welfare as well as fair solutions maximizing the utility of the least happy agent. We show that many problems can be expressed in this setting, such as fair division of indivisible goods, some multiwinner elections, or multifacility location. We focus on the complexity of finding optimal solutions, and we identify the tractability boarder between polynomial and NP-hard settings, along several parameters: the syntax of formulas, the allowed weights, as well as the number of agents, propositional symbols, and formulas per agent.

Boolean games are a game-theoretic framework in which propositional logic is used to describe agents’ goals. In this paper we investigate how agents in Boolean games can reach an efficient and fair outcome through a simple negotiation protocol. We are particularly interested in settings where agents only have incomplete knowledge about the preferences of others. After explaining how generalized possibilistic logic can be used to compactly encode such knowledge, we analyze how a lack of knowledge affects the agreement outcome. In particular, we show how knowledgeable agents can obtain a more desirable outcome than others.

To engage diverse agents in cooperative behavior, it is important, even necessary, to provide algorithms that do not reveal information that is private or proprietary.A number of recent planning algorithms enable agents to plan together for shared goals without disclosing information about their private state and actions. But these algorithms lack clear and formal privacy guarantees: the fact that they do not require agents to explicitly reveal private information, does not imply that such information cannot be deduced. The main contribution of this paper is an enhanced version of the distributed forward-search planning framework of Nissim and Brafman that reveals less information than the original algorithm, and the first, to our knowledge, discussion and formal proof of privacy guarantees for distributed planning and search algorithms.

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 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.

Standard results on and algorithms for repeated games assume that defections are instantly observable. In reality, it may take some time for the knowledge that a defection has occurred to propagate through the social network. How does this affect the structure of equilibria and algorithms for computing them? In this paper, we consider games with cooperation and defection. We prove that there exists a unique maximal set of forever-cooperating agents in equilibrium and give an efficient algorithm for computing it. We then evaluate this algorithm on random graphs and find experimentally that there appears to be a phase transition between cooperation everywhere and defection everywhere, based on the value of cooperation and the discount factor. Finally, we provide a condition for when the equilibrium found is credible, in the sense that agents are in fact motivated to punish deviating agents. We find that this condition always holds in our experiments, provided the graphs are sufficiently large.

Animals in social foraging not only present the ordered and aggregated group movement but also the individual movement patterns of Lévy walks that are characterized as the power-law frequency distribution of flight lengths. The environment and the conspecific effects between group members are two fundamental inducements to the collective behavior. However, most previous models emphasize one of the two inducements probably because of the great difficulty to solve the behavior conflict caused by two inducements. Here, we propose an environment-driven social force model to simulate overall foraging process of an agent group. The social force concept is adopted to quantify the conspecific effects and the interactions between individuals and the environment. The cohesion-first rule is implemented to solve the conflict, which means that individuals preferentially guarantee the collective cohesion under the environmental effect. The obtained results efficiently comply with the empirical reports that mean the Lévy walk pattern of individual movement paths and the high consistency and cohesion of the entity group. By extensive simulations, we also validate the impact of two inducements for individual behaviors in comparison with several classic models

In the contemporary autonomous systems the role of complex interactions such as (possibly relaxed) dialogues is increasing significantly. In this paper we provide a paraconsistent and paracomplete implementation of inquiry dialogue under realistic assumptions regarding availability and quality of information. Various strategies for dealing with unsure and inconsistent information are analyzed. The corresponding dialogue outcomes are further evaluated against the (paraconsistent and paracomplete) distributed beliefs of the group. A specific 4-valued logic underpins the presented framework. Thanks to the qualities of the implementation tool: a rule-based query language 4QL, our solution is both expressive and tractable.