When voters elicit ranked preferences over candidates, one particular axiom for proportional representation is Proportionality of Solid Coalitions (PSC). This axiom was advocated by Dummett [4] and has been referred to as the most important requirement for proportional representation [15, 16, 18, 19]. PSC is the subject of many theoretical and empirical studies. Theoretical studies have focused on designing voting rules that satisfy PSC; these include single transferable vote (STV) [15], Quota Borda System (QBS) [4], Schulz-STV [14], and the Expanding Approvals Rule (EAR) [2].

Inceoglu I, Thomas G, Chu C, Plans D, Gerbasi A (2018). Leadership behavior and employee well-being: an integrated review and a future research agenda. Lopez D, Brown AW, Plans D. (2019). Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multiagent system. Murphy J, Brewer R, Coll M-P, Plans D, Hall M, Shiu SS, Catmur C, Bird G. (2019).

Facebook has recently created an algorithm that enhances an AI agent's ability to navigate an environment, letting the agent determine the shortest route through new environments without access to a map. While mobile robots typically have a map programmed into them, the new algorithm that Facebook designed could enable the creation of robots that can navigate environments without the need for maps. According to a post created by Facebook researchers, a major challenge for robot navigation is endowing AI systems with the ability to navigate through novel environments and reaching programmed destinations without a map. In order to tackle this challenge, Facebook created a reinforcement learning algorithm distributed across multiple learners. The algorithm was called decentralized distributed proximal policy optimization (DD-PPO).

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.

In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can facilitate seamless energy supply. However, the risk associated with energy supply is also increased due to unpredictable energy generation from renewable and non-renewable sources. Especially, the risk of energy shortfall is involved with uncertainties in both energy consumption and generation. In this paper, we study a risk-aware energy scheduling problem for a microgrid-powered MEC network. First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the loss of energy shortfall of the MEC networks and we show this problem is an NP-hard problem. Second, we analyze our formulated problem using a multi-agent stochastic game that ensures the joint policy Nash equilibrium, and show the convergence of the proposed model. Third, we derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based asynchronous advantage actor-critic (A3C) algorithm with shared neural networks. This method mitigates the curse of dimensionality of the state space and chooses the best policy among the agents for the proposed problem. Finally, the experimental results establish a significant performance gain by considering CVaR for high accuracy energy scheduling of the proposed model than both the single and random agent models.

The Web is ubiquitous, increasingly populated with interconnected data, services, people, and objects. Semantic web technologies (SWT) promote uniformity of data formats, as well as modularization and reuse of specifications (e.g., ontologies), by allowing them to include and refer to information provided by other ontologies. In such a context, multi-agent system (MAS) technologies are the right abstraction for developing decentralized and open Web applications in which agents discover, reason and act on Web resources and cooperate with each other and with people. The aim of the project is to propose an approach to transform "Agent and artifact (A&A) meta-model" into a Web-readable format with ontologies in line with semantic web formats and to reuse already existing ontologies in order to provide uniform access for agents to things.

The problem of multi-agent task allocation arises in a variety of scenarios involving human teams. In many such settings, human teammates may act with selfish motives and try to minimize their cost metrics. In the absence of (1) complete knowledge about the reward of other agents and (2) the team's overall cost associated with a particular allocation outcome, distributed algorithms can only arrive at sub-optimal solutions within a reasonable amount of time. To address these challenges, we introduce the notion of an AI Task Allocator (AITA) that, with complete knowledge, comes up with fair allocations that strike a balance between the individual human costs and the team's performance cost. To ensure that AITA is explicable to the humans, we allow each human agent to question AITA's proposed allocation with counterfactual allocations. In response, we design AITA to provide a replay negotiation tree that acts as an explanation showing why the counterfactual allocation, with the correct costs, will eventually result in a sub-optimal allocation. This explanation also updates a human's incomplete knowledge about their teammate's and the team's actual costs. We then investigate whether humans are (1) able to understand the explanations provided and (2) convinced by it using human factor studies. Finally, we show the effect of various kinds of incompleteness on the length of explanations. We conclude that underestimation of other's costs often leads to the need for explanations and in turn, longer explanations on average.

Today organizations have to deal with so many emergent behaviors that the notion of central control as the only coping mechanism seems to be receding as a dominant management model. Freedom must be doled out further from the centrist idea by creating goals, constraints, boundaries and allowable edge behaviors. Someday software and hardware agents will negotiate their contribution to business outcomes on their own, but until then organizations will have to prepare themselves by managing coordinated autonomy. Edge computing is a form of distributed computing which brings computation and data storage closer to the location where it is needed, to improve response times and provide better actions. Now, AI on Edge, can offer a whole lot of new possibilities.

The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a BS unit can act as a self-powered BS. In this paper, an effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. First, a two-stage linear stochastic programming problem is formulated with the goal of minimizing the total energy consumption cost of the system while fulfilling the energy demand. Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem. In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy dispatch decision by accepting only the observations from each local agent with its own state information. Meanwhile, each BS agent estimates its own energy dispatch policy by applying the learned parameters from meta-agent. Finally, the proposed MAMRL framework is benchmarked by analyzing deterministic, asymmetric, and stochastic environments in terms of non-renewable energy usages, energy cost, and accuracy. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost (with 95.8% prediction accuracy), compared to other baseline methods.

In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG). We generalize existing results of Poincar\'e recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting the reward (by adding a regularization term) of the game can give strong convergence guarantees in monotone games. We continue by showing how this reward adaptation technique can be leveraged to build algorithms that converge exactly to the Nash equilibrium. Finally, we show how these insights can be directly used to build state-of-the-art model-free algorithms for zero-sum two-player Imperfect Information Games (IIG).