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de Nijs, F. (author), de Weerdt, M.M. (author), Spaan, M.T.J. (author)Demand response refers to the concept that power consumption should aim to match supply, instead of supply following demand. It is a key technology to enable the successful transition to an electricity system that incorporates more and more intermittent and uncontrollable renewable energy sources. For instance, loads such as heat pumps or...book chapter 2019
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Scharpff, J.C.D. (author), Spaan, M.T.J. (author), De Weerdt, M.M. (author)We study the planning of maintenance activities on public infrastructural networks – road networks, Internet, power grids, etc. – in contingent environments such that the negative impact on the network user is minimised. Traditional efforts hereto are mainly of a regulatory nature, whereas we propose charging the service-providers (agents)...conference paper 2012
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Oliehoek, F.A. (author), Whiteson, S. (author), Spaan, M.T.J. (author)Dec-POMDPs are a powerful framework for planning in multiagent systems, but are provably intractable to solve. This paper proposes a factored forward-sweep policy computation method that tackles the stages of the problem one by one, exploiting weakly coupled structure at each of these stages. An empirical evaluation shows that the loss in...conference paper 2013
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Walraven, E.M.P. (author), Spaan, M.T.J. (author)Integration of renewable energy in power systems is a potential source of uncertainty, because renewable generation is variable and may depend on changing and highly uncertain weather conditions. In this paper we present and evaluate a new method to schedule power-demanding tasks with release times and deadlines under uncertainty, in order to...conference paper 2015
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Spaan, M.T.J. (author), Oliehoek, F.A. (author), Amato, C. (author)We advance the state of the art in optimal solving of decentralized partially observable Markov decision processes (Dec-POMDPs), which provide a formal model for multiagent planning under uncertainty.conference paper 2011
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Scharpff, J.C.D. (author), Spaan, M.T.J. (author), Volker, L. (author), De Weerdt, M.M. (author)Scheduling of infrastructural maintenance poses a complex multi-agent problem. Commonly a central authority is responsible for the quality and throughput of the infrastructure, while the actual maintenance is performed by multiple self-interested contractors. Not only does the central authority have to (economically) incentivise agents to...conference paper 2013
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Scharpff, J.C.D. (author), Spaan, M.T.J. (author), Volker, L. (author), De Weerdt, M.M. (author)Scheduling of infrastructural maintenance poses a complex multi-agent problem. Commonly a central authority is responsible for the quality and throughput of the infrastructure, while the actual maintenance is performed by multiple self-interested contractors. Not only does the central authority have to (economically) incentivise agents to...conference paper 2013
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Walraven, E.M.P. (author), Spaan, M.T.J. (author)The increasing penetration of renewable energy sources and electric vehicles raises important challenges related to the operation of electricity grids. For instance, the amount of power generated by wind turbines is time-varying and dependent on the weather, which makes it hard to match flexible electric vehicle demand and uncertain wind power...conference paper 2016
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Elffers, J. (author), Konijnenberg, D. (author), Walraven, E.M.P. (author), Spaan, M.T.J. (author)Several approaches exist to solve Artificial Intelligence planning problems, but little attention has been given to the combination of using landmark knowledge and satisfiability (SAT). Landmark knowledge has been exploited successfully in the heuristics of classical planning. Recently it was also shown that landmark knowledge can improve the...conference paper 2013
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Walraven, E.M.P. (author), Spaan, M.T.J. (author)In many planning domains external factors are hard to model using a compact Markovian state. However, long-term dependencies between consecutive states of an environment might exist, which can be exploited during planning. In this paper we propose a scenario representation which enables agents to reason about sequences of future states. We show...conference paper 2015
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Scharpff, J. (author), Spaan, M.T.J. (author), Volker, L. (author), De Weerdt, M.M. (author)We address efficient planning of maintenance activities in infrastructural networks, inspired by the real-world problem of servicing a highway network. A road authority is responsible for the quality, throughput and maintenance costs of the network, while the actual maintenance is performed by autonomous, third-party contractors. From a (multi...conference paper 2013
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Oliehoek, F.A. (author), Whiteson, S. (author), Spaan, M.T.J. (author)Dec-POMDPs are a powerful framework for planning in multiagent systems, but are provably intractable to solve. This paper proposes a factored forward-sweep policy computation method that tackles the stages of the problem one by one, exploiting weakly coupled structure at each of these stages. An empirical evaluation shows that the loss in...conference paper 2013
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Spaan, M.T.J. (author), Oliehoek, F.A. (author)Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful framework for optimal decision making under the assumption of instantaneous communication. We focus on a delayed communication setting (MPOMDP-DC), in which broadcasted information is delayed by at most one time step. In this paper, we show that computation of...conference paper 2012
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Elffers, J. (author), Konijnenberg, D. (author), Walraven, E. (author), Spaan, M.T.J. (author)Several approaches exist to solve Artificial Intelligence planning problems, but little attention has been given to the combination of using landmark knowledge and satisfiability (SAT). Landmark knowledge has been exploited successfully in the heuristics of classical planning. Recently it was also shown that landmark knowledge can improve the...conference paper 2013
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Carr, Steven (author), Jansen, Nils (author), Bharadwaj, Suda (author), Spaan, M.T.J. (author), Topcu, Ufuk (author)We study planning problems where a controllable agent operates under partial observability and interacts with an uncontrollable opponent, also referred to as the adversary. The agent has two distinct objectives: To maximize an expected<br/>value and to adhere to a safety specification. Multi-objective partially observable stochastic games (POSGs...conference paper 2021
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- Ponnambalam, C.T. (author), Kamran, Danial (author), Simão, T. D. (author), Oliehoek, F.A. (author), Spaan, M.T.J. (author) conference paper 2022
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Suau, M. (author), He, J. (author), Spaan, M.T.J. (author), Oliehoek, F.A. (author)Learning effective policies for real-world problems is still an open challenge for the field of reinforcement learning (RL). The main limitation being the amount of data needed and the pace at which that data can be obtained. In this paper, we study how to build lightweight simulators of complicated systems that can run sufficiently fast for...conference paper 2022
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Neustroev, G. (author), Ponnambalam, C.T. (author), de Weerdt, M.M. (author), Spaan, M.T.J. (author)Reinforcement learning requires exploration, leading to repeated execution of sub-optimal actions. Naive exploration techniques address this problem by changing gradually from exploration to exploitation. This approach employs a wide search resulting in exhaustive exploration and low sample-efficiency. More advanced search methods explore...conference paper 2020
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Yang, Q. (author), Spaan, M.T.J. (author)Without an assigned task, a suitable intrinsic objective for an agent is to explore the environment efficiently. However, the pursuit of exploration will inevitably bring more safety risks.<br/>An under-explored aspect of reinforcement learning is how to achieve safe efficient exploration when the task is unknown.<br/>In this paper, we propose a...conference paper 2023
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Suau, M. (author), He, J. (author), Çelikok, Mustafa Mert (author), Spaan, M.T.J. (author), Oliehoek, F.A. (author)Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly complex dynamics, which makes their full-scale simulation computationally slow. In this paper, we show how to factorize large networked systems of many agents into...conference paper 2022