124 research outputs found
Sharing rides with friends: a coalition formation algorithm for ridesharing
We consider the Social Ridesharing (SR) problem, where a set of commuters, connected through a social network, arrange one-time rides at short notice. In particular, we focus on the associated optimisation problem of forming cars to minimise the travel cost of the overall system modelling such problem as a graph constrained coalition formation (GCCF) problem, where the set of feasible coalitions is restricted by a graph (i.e., the social network). Moreover, we significantly extend the state of the art algorithm for GCCF, i.e., the CFSS algorithm, to solve our GCCF model of the SR problem. Our empirical evaluation uses a real dataset for both spatial (GeoLife) and social data (Twitter), to validate the applicability of our approach in a realistic application scenario. Empirical results show that our approach computes optimal solutions for systems of medium scale (up to 100 agents) providing significant cost reductions (up to -36.22%). Moreover, we can provide approximate solutions for very large systems (i.e., up to 2000 agents) and good quality guarantees (i.e., with an approximation ratio of 1.41 in the worst case) within minutes (i.e., 100 seconds
Evaluating semi-automatic annotation of domestic energy consumption as a memory aid
Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants
Outlining the design space of eXplainable swarm (xSwarm): experts perspective
In swarm robotics, agents interact through local roles to solve complex tasks
beyond an individual's ability. Even though swarms are capable of carrying out
some operations without the need for human intervention, many safety-critical
applications still call for human operators to control and monitor the swarm.
There are novel challenges to effective Human-Swarm Interaction (HSI) that are
only beginning to be addressed. Explainability is one factor that can
facilitate effective and trustworthy HSI and improve the overall performance of
Human-Swarm team. Explainability was studied across various Human-AI domains,
such as Human-Robot Interaction and Human-Centered ML. However, it is still
ambiguous whether explanations studied in Human-AI literature would be
beneficial in Human-Swarm research and development. Furthermore, the literature
lacks foundational research on the prerequisites for explainability
requirements in swarm robotics, i.e., what kind of questions an explainable
swarm is expected to answer, and what types of explanations a swarm is expected
to generate. By surveying 26 swarm experts, we seek to answer these questions
and identify challenges experts faced to generate explanations in Human-Swarm
environments. Our work contributes insights into defining a new area of
research of eXplainable Swarm (xSwarm) which looks at how explainability can be
implemented and developed in swarm systems. This paper opens the discussion on
xSwarm and paves the way for more research in the field.Comment: In the 16th International Symposium on Distributed Autonomous Robotic
Systems 2022, November 28-30, 2022, Montbeliard, Franc
Balanced trade reduction for dual-role exchange markets
We consider dual-role exchange markets, where traders can offer to both buy and sell the same commodity in the exchange but, if they transact, they can only be either a buyer or a seller, which is determined by the market mechanism. To design desirable mechanisms for such exchanges, we show that existing solutions may not be incentive compatible, and more importantly, cause the market maker to suffer a significant deficit. Hence, to combat this problem, following McAfee's trade reduction approach, we propose a new trade reduction mechanism, called balanced trade reduction, that is incentive compatible and also provides flexible trade-offs between efficiency and defici
Algorithms for Graph-Constrained Coalition Formation in the Real World
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 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 12-core 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).Comment: Accepted for publication, cite as "in press
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