In this work, we adopt a cooperative game theoretic approach in order to tackle the social ridesharing (SR) problem, where a set of commuters, connected through a social network, form coalitions and arrange one-time rides at short
Matching demand and supply is recognized as a crucial issue for smart grids, and ICT-based solutions are essential to deliver the infrastructure, algorithms and mechanisms for demand-supply balancing. To date, most work in this area focus on providing users with real time feedback on energy prices and consumption, or on load scheduling of home appliances for individual user consumption. In this paper, we take a complementary approach by exploiting social relationships among consumers to organise them into coalitions of Virtual Electricity Consumers (VECs) that buy electricity as a single customer in order to get a discount on electricity through collective buying. Specifically, we model our problem as a coalitional game and provide an algorithm, based on linear programming, to form VECs. The VECs formed by our algorithm are both efficient (i.e., minimizing the sum of users' payments) and stable (i.e., no user has any incentive to break away). We empirically analyse our approach using real consumption data for a set of households located in UK. Our analysis provides interesting insights into the relationship between structure and stability of VEC's and prices within the electricity market.
Coalition formation is a fundamental approach to multi-agent coordination, and a key challenge in this context is the coalition structure generation problem, where a set of agents must be partitioned into the best set of coalitions. This problem is NP-hard and typical optimal algorithms do not scale to more than 50 agents: efficient approximate solutions are therefore needed for hundreds or thousands of agents. In this paper we propose a novel heuristic, based on ideas and tools used in the data clustering domain. In particular, we present a coalition formation algorithm inspired by the well known class of hierarchical agglomerative clustering techniques (Linkage algorithms). We present different variants of the algorithm, which we call Coalition Linkage (C-Link) and demonstrate how such algorithm can be adapted to graph restricted coalition formation problems (where an interaction graph defined among the agents restricts the set of feasible coalitions). Moreover, we discuss how we can provide an upper bound on the value of the optimal coalition structure, and we show that for specific characteristic functions we can provide such bounds while maintaining polynomial computational costs and memory requirements. We empirically evaluate the different variants of the CLink algorithm on two synthetic benchmark data-sets, as well as in two real world scenarios, involving a collective energy purchasing and a ride-sharing application. In these settings C-Link achieves promising results providing high quality solutions and solving problem involving thousands of agents in few minutes.
Peer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-topeer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO 2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO 2 emissions and up to 80.08% in traffic congestion.
Coalition formation typically involves the coming together of multiple, heterogeneous, agents to achieve both their individual and collective goals. In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions. We focus on this type of problem given that in many real-world applications, agents may be connected by a communication network or only trust certain peers in their social network. We propose a novel representation of this problem based on the concept of edge contraction, which allows us to model the search space induced by the GCCF problem as a rooted tree. Then, we propose an anytime solution algorithm (CFSS), which is particularly efficient when applied to a general class of characteristic functions called m + a functions. Moreover, we show how CFSS can be efficiently parallelised to solve GCCF using a non-redundant partition of the search space. We benchmark CFSS on both synthetic and realistic scenarios, using a real-world dataset consisting of the energy consumption of a large number of households in the UK. Our results show that, in the best case, the serial version of CFSS is 4 orders of magnitude faster than the state of the art, while the parallel version is 9.44 times faster than the serial version on a 12core machine. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems of agents (i.e., with more than 2700 agents).
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