Abstract: Researchers that study Human-Like computing mainly aim to understand how systems can emulate human cognitive performance. However, when Human-Like systems are designed for sharing economy applications in which humans have to collaborate in order to achieve a desired task, there are several problems that needs to be addressed before asking how cognitive performance of a single human can be emulated. In this paper, we highlight these problems, provide examples related to the ridesharing scenario, and introduce how we approach these problems and which techniques we are using to tackle them.

Citation: S.Ceppi. The Dilemma of Human-Like Collective Systems. In S. Muggleton et al, editor, Twentieth Workshop on Machine Intelligence (MI 20), Windsor Park, UK, 23-25 October, 2016. In press.

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Abstract: Vehicle-to-grid (V2G) is a promising approach whereby electric vehicles (EVs) are used to store excess electricity supply (e.g., from renewable sources), which is sold back to the grid in times of scarcity. In this paper we consider the setting of a smart car park, where EVs come and go, and can be used for V2G while parked. We develop novel allocation and payment mechanisms which truthfully elicit the EV owners’ preferences and constraints, including arrival, departure, required charge, as well as the costs of discharging due to loss of efficiency of the battery. The car park will schedule the charging and discharging of each EV, ensuring the constraints of the EVs are met, and taking into consideration predictions about future electricity prices. Optimally solving the global problem is intractable, and we present three novel heuristic online scheduling algorithms. We show that, under certain conditions, two of these satisfy monotonicity and are therefore truthful. We furthermore evaluate the algorithms using simulations, and we show that some of our algorithms benefit significantly from V2G, achieving positive benefit for the car park even when agents do not pay for using it.

Citation: Enrico H. Gerding, Sebastian Stein, Sofia Ceppi and Valentin Robu. Online Mechanism Design for Vehicle-to-Grid Car Parks. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI). New York, USA, July 2016.

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Abstract: Sharing economy applications need to coordinate humans, each of whom may have different preferences over the provided service. Traditional approaches model this as a resource allocation problem and solve it by identifying matches between users and resources. These require knowledge of user preferences and, crucially, assume that they act deterministically or, equivalently, that each of them is expected to accept the proposed match. This assumption is unrealistic for applications like ridesharing and house sharing (like airbnb), where user coordination requires handling of the diversity and uncertainty in human behaviour.
We address this shortcoming by proposing a diversity-aware recommender system that leaves the decision-power to users but still assists them in coordinating their activities. We achieve this through taxation, which indirectly modifies users’ preferences over options by imposing a penalty on them. This is applied on options that, if selected, are expected to lead to less favourable outcomes, from the perspective of the collective. The framework we used to identify the options to recommend is composed by three optimisation steps, each of which has a mixed integer linear program at its core. Using a combination of these three programs, we are also able to compute solutions that permit a good trade-off between satisfying the global goals of the collective and the individual users’ interests. We demonstrate the effectiveness of our approach with two experiments in a simulated ridesharing scenario, showing: (a) significantly better coordination results with the approach we propose, than with a set of recommendations in which taxation is not applied and each solution maximises the goal of the collective, (b) that we can propose a recommendation set to users instead of imposing them a single allocation at no loss to the collective, and (c) that our system allows for an adaptive trade-off between conflicting criteria.

Citation: P. Andreadis, S. Ceppi, M. Rovatsos, and S. Ramamoorthy. Diversity-Aware Recommendation for Human Collectives. In Proceedings of the 1st International Workshop on Diversity-Aware Artificial Intelligence (DIVERSITY 2016), The Hague, The Netherlands, 2016

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Abstract: Web-based collaborative systems, where most computation is performed by human collectives, have distinctly different requirements from traditional workflow orchestration systems, as humans have to be mobilised to perform computations and the system has to adapt to their collective behaviour at runtime. In this paper, we present a social orchestration system called SmartOrch, which has been designed specifically for collective adaptive systems in which human participation is at the core of the overall distributed computation. SmartOrch provides a flexible and customisable workflow composition framework that has multi-level optimisation capabilities. These features allow us to manage the uncertainty that collective adaptive systems need to deal with in a principled way.
We demonstrate the benefits of SmartOrch with simulation experiments in a ridesharing domain. Our experiments show that SmartOrch is able to respond flexibly to variation
in collective human behaviour, and to adapt to observed behaviour at different levels. This is accomplished by learning how to propose and route human-based tasks, how to allocate computational resources when managing these tasks, and how to adapt the overall interaction model of the platform based on past performance. By proposing novel, solid engineering principles for these kinds of systems, SmartOrch addresses shortcomings of previous work that mostly focused on application-specific, non-adaptive solutions.

Citation: M. Rovatsos, D. Diochnos, Z. Wen, S. Ceppi, and P. Andreadis. SmartOrch: An Adaptive Orchestration System for Human-Machine Collectives. In Proceedings of the Special Track on Collective Adaptive Systems of the 32nd ACM Symposium on Applied Computing (SAC2017), Marrakech, Morocco, 2017. In Press

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