Two principles of enduring institutions for selforganising resource allocation are congruence of the allocation method to the resources available, and participation of those affected by the allocation (the appropriators) in selecting that method. However, the principles do not say anything explicitly about the fairness of the allocation method, or the outcomes. In this paper, we complement these principles with canons of distributive justice represented as legitimate claims, which are implemented as voting functions that determine the order in which resource requests are satisfied. The appropriators vote on the weight attached to the scoring functions, and so selforganise the allocation method. Experiments with a variation of the Linear Public Good game show that this pluralistic selforganising approach produces a better balance of utility and fairness (for agents that comply with the rules of the game) than monistic or fixed approaches.
Auctions have been used to deal with resource allocation in multiagent environments, especially in serviceoriented electronic markets. In this type of market, resources are perishable and auctions are repeated over time with the same or a very similar set of agents. In this scenario it is advisable to use recurrent auctions: a sequence of auctions of any kind where the result of one auction may influence the following one. Some problems do appear in these situations, as for instance, the bidder drop problem, the asymmetric balance of negotiation power or resource waste, which could cause the market to collapse. Fair mechanisms can be useful to minimize the effects of these problems. With this aim, we have analyzed four previous fair mechanisms under dynamic scenarios and we have proposed a new one that takes into account changes in the supply as well as the presence of alternative marketplaces. We experimentally show how the new mechanism presents a higher average performance under all simulated conditions, resulting in a higher profit for the auctioneer than with the previous ones, and in most cases avoiding the waste of resources.
Mobility as a Service (MaaS) is the integrated and on-demand offering of new mode-sharing transport schemes, such as ride-share, car-share or car-pooling. MaaS schemes may solve some of the most pressing mobility problems in large conurbations like London. However, MaaS schemes pose significant implementation challenges for operators and city authorities alike. With the existing transport and traffic planning tools, even basic questions do not have easy answers: e.g. how many vehicles are needed; how should they be deployed; what infrastructure changes are needed, and what will happen with congestion? This paper reports on the novel integration, through co-simulation of two independent agent-based simulators: MATSim and IMSim. MATSim generates realistic transport demand for a city: allocating travellers to the best mobility option according to their preferences; while IMSim provides a highly realistic operational execution of autonomous and manually driven transport fleets. We show how the simulation tools complement each other to deliver a superior Autonomous Mobility on Demand (AMoD) modelling capability. By combining the two, we can evaluate the impact of diverse AMoD scenarios from different standpoints: from a traveller's perspective (e.g. satisfaction, service level, etc.); from an operator's perspectives (e.g. cost, revenue, etc.); and from a city's perspective (e.g. congestion, significant shifts between transport modes, etc.). The coupled simulation methods have underpinned the extensive MERGE Greenwich project investigation into the challenges of offering ride-share services in autonomous vehicles in the Royal Borough of Greenwich (London, UK) for travellers, service-operators, the city, and vehicle manufacturers.
This paper presents a robot search task (social tag) that uses social interaction, in the form of asking for help, as an integral component of task completion. Socially distributed perception is defined as a robot's ability to augment its limited sensory capacities through social interaction. We describe the task of social tag and its implementation on the robot GRACE for the AAAI 2005 Mobile Robot Competition & Exhibition. We then discuss our observations and analyses of GRACE's performance as a situated interaction with conference participants. Our results suggest we were successful in promoting a form of social interaction that allowed people to help the robot achieve its goal. Furthermore, we found that different social uses of the physical space had an effect on the nature of the interaction. Finally, we discuss the implications of this design approach for effective and compelling human-robot interaction, considering its relationship to concepts such as dependency, mixed initiative, and socially distributed cognition.
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