641 results found
Jennings N, Augustin A, Venanzi M, et al., Bayesian aggregation of categorical distributions with applications in crowdsourcing, Proc. 25th Int. Joint Conf. on AI, ISSN: 1045-0823
Jennings N, Stein S, Gerding EH, et al., Evaluating market user interfaces for electric vehicle charging using Bid2Charge, Proc. 25th Int. Joint Conf. on AI, ISSN: 1045-0823
Jennings N, Waniek M, Tran-Thanh L, et al., The dollar auction with spiteful players, Proc. 31st AAAI Conference on Artificial Intelligence, Publisher: American Association for Artificial Intelligence (AAAI) Press
Jennings N, Zhang Y, An B, et al., Optimal escape interdiction on transportation networks, Proc. 25th Int. Joint Conf. on AI, ISSN: 1045-0823
Alkayal ES, Jennings NR, Abulkhair MF, 2017, Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing, Pages: 17-24
© 2016 IEEE. Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers' requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization (PSO) algorithm. In more detail, we develop a novel multi-objective PSO (MOPSO) algorithm, based on a new ranking strategy. The main insight of this algorithm is that the tasks are scheduled to the virtual machines to minimize waiting time and maximize system throughput. The algorithm leads to a reduction in execution time of 20%, a reduction the waiting time of 30%, and shows improvements of up to 40% in throughput compared to the current state of the art.
Panagopoulos AA, Maleki S, Rogers A, et al., 2017, Advanced economic control of electricity-based space heating systems in domestic coalitions with shared intermittent energy resources, ACM Transactions on Intelligent Systems and Technology, Vol: 8, ISSN: 2157-6904
© 2017 ACM. Over the past few years, Domestic Heating Automation Systems (DHASs) that optimize the domestic space heating control process with minimum user input, utilizing appropriate occupancy prediction technology, have emerged as commercial products (e.g., the smart thermostats from Nest and Honeywell). At the same time, many houses are being equipped with, potentially grid-connected, Intermittent Energy Resources (IERs), such as rooftop photovoltaic systems and/or small wind turbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than the price of bu ying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASs need to incorporate Advanced Economic Control (AEC). Such AEC can exploit the energy buffer that heating loads provide, in order to shift the consumption of electricity-based heating systems to follow the intermittent energy generation of the house. By so doing, the energy imported from the grid can be minimized and considerable monetary gains for the household can be achieved, without affecting the occupants' schedule. These benefits can be amplified still further in domestic coalitions, where a number of houses come together and share their IER generation to minimize their cumulative grid energy import. Given the above, in this work we extend a state-of-The-Art DHAS, to propose AdaHeat+, a practical DHAS, that, for the first time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes complete with an allocation mechanism to share the coalition gains. Importantly, we propose an effective heuristic heating schedule planning approach for collective AEC that (i) has a complexity that scales in a linear and parallelizable manner with the coalition size, and (ii) enables AdaHeat+ to handle the distinct preferences, in balancing heating cost and thermal discomfort, of the households. Our approa
Robu V, Vinyals M, Rogers A, et al., 2017, Efficient Buyer Groups with Prediction-of-Use Electricity Tariffs, IEEE Transactions on Smart Grid, Pages: 1-1, ISSN: 1949-3053
We consider settings where owners of electric vehicles (EVs) participate in a market mech-anism to charge their vehicles. Existing work on such mechanisms has typically assumedthat participants are fully rational and can report their preferences accurately via someinterface to the mechanism or to a software agent participating on their behalf. How-ever, this may not be reasonable in settings with non-expert human end-users.Thus, ouroverarching aim in this paper is to determine experimentally if a fully expressive marketinterface that enables accurate preference reports is suitable for the EV charging domain,or, alternatively, if a simpler, restricted interface that reduces the space of possible optionsis preferable. In doing this, we measure the performance of an interface both in terms ofhow it helps participants maximise their utility and how it affects deliberation time. Oursecondary objective is to contrast two different types of restricted interfaces that vary inhow they restrict the space of preferences that can be reported. To enable this analysis,we develop a novel game that replicates key features of an abstract EV charging scenario.In two experiments with over 300 users, we show that restricting the users’ preferencessignificantly reduces the time they spend deliberating (by up to half in some cases). Anextensive usability survey confirms that this restriction is furthermore associated with alower perceived cognitive burden on the users. More surprisingly, at the same time, usingrestricted interfaces leads to an increase in the users’ performance compared to the fullyexpressive interface (by up to 70%). We also show that some restricted interfaces havethe desirable effect of reducing the energy consumption of their users by up to 20% whileachieving the same utility as other interfaces. Finally, we find that a reinforcement learningagent displays similar performance trends to human user
Alan AT, Costanza E, Ramchurn SD, et al., 2016, Tariff Agent: Interacting with a Future Smart Energy System at Home, ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION, Vol: 23, ISSN: 1073-0516
Alkayal ES, Jennings NR, Abulkhair MF, 2016, Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing, 41st IEEE Conference on Local Computer Networks (LCN), Publisher: IEEE, Pages: 17-24
Baker CAB, Ramchurn S, Teacy WTL, et al., 2016, Planning Search and Rescue Missions for UAV Teams, 22nd European Conference on Artificial Intelligence (ECAI), Publisher: IOS PRESS, Pages: 1777-1782, ISSN: 0922-6389
Beck Z, Teacy L, Rogers A, et al., 2016, Online planning for collaborative search and rescue by heterogeneous robot teams, Pages: 1024-1032, ISSN: 1548-8403
Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. Collaboration is essential for effective performance by groups of robots in disaster response settings. Here we are particularly interested in heterogeneous robots that collaborate in complex scenarios with incomplete, dynamically changing information. In detail, we consider a search and rescue setting, where robots with different capabilities work together to accomplish tasks (rescue) and find information about further tasks (search) at the same time. The state of the art for such collaboration is robot control based on independent planning for robots with different capabilities and typically incorporates uncertainty with only a limited scope. In contrast, in this paper, we create a joint plan to optimise all robots' actions incorporating uncertainty about the future information gain of the robots. We evaluate our planner's performance in settings based on real disasters and find that our approach decreases the response time by 20-25% compared to state-of-the-art approaches. In addition, practical constraints are met in terms of time and resource utilisation.
Michalak T, Rahwan T, Elkind E, et al., 2016, A hybrid exact algorithm for complete set partitioning, ARTIFICIAL INTELLIGENCE, Vol: 230, Pages: 14-50, ISSN: 0004-3702
Obraztsova S, Rabinovich Z, Elkind E, et al., 2016, Trembling hand equilibria of plurality voting, Pages: 440-446, ISSN: 1045-0823
Trembling hand (TH) equilibria were introduced by Selten in 1975. Intuitively, these are Nash equilibria that remain stable when players assume that there is a small probability that other players will choose off-equilibrium strategies. This concept is useful for equilibrium refinement, i.e., selecting the most plausible Nash equilibria when the set of all Nash equilibria can be very large, as is the case, for instance, for Plurality voting with strategic voters. In this paper, we analyze TH equilibria of Plurality voting. We provide an efficient algorithm for computing a TH best response and establish many useful properties of TH equilibria in Plurality voting games. On the negative side, we provide an example of a Plurality voting game with no TH equilibria, and show that it is NP-hard to check whether a given Plurality voting game admits a TH equilibrium where a specific candidate is among the election winners.
Powell R, An B, Jennings NR, et al., 2016, On the Vulnerability of Outlier Detection Algorithms in Smart Traffic Control Systems, 7th International Conference on Decision and Game Theory for Security (GameSec), Publisher: SPRINGER INT PUBLISHING AG, Pages: 474-475, ISSN: 0302-9743
Pruna RT, Polukarov M, Jennings NR, 2016, An Asset Pricing Model with Loss Aversion and its Stylized Facts, IEEE Symposium Series on Computational Intelligence (IEEE SSCI), Publisher: IEEE
Ramchurn SD, Trung DH, Wu F, et al., 2016, A Disaster Response System based on Human-Agent Collectives, JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, Vol: 57, Pages: 661-708, ISSN: 1076-9757
Robu V, Chalkiadakis G, Kota R, et al., 2016, Rewarding cooperative virtual power plant formation using scoring rules, ENERGY, Vol: 117, Pages: 19-28, ISSN: 0360-5442
Shi B, Gerding EH, Jennings NR, 2016, An equilibrium analysis of trading across multiple double auction marketplaces using fictitious play, ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, Vol: 17, Pages: 134-149, ISSN: 1567-4223
Stein S, Gerding EH, Nedea A, et al., 2016, Bid2Charge: Market user interface design for electric vehicle charging, Pages: 882-890, ISSN: 1548-8403
Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users. To explore this, we compare a fully expressive interface that covers the entire space of preferences to two restricted interfaces that reduce the space of possible options. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two extensive evaluations with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating. More surprisingly, it also leads to an increase in their utility compared to the fully expressive interface (up to 70%). Finally, we find that a reinforcement learning agent displays similar performance trends, enabling a novel methodology for evaluating market interfaces.
Venanzi M, Guiver J, Kohli P, et al., 2016, Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems, JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, Vol: 56, Pages: 517-545, ISSN: 1076-9757
Xu H, Tran-Thanh L, Jennings NR, 2016, Playing repeated security games with no prior knowledge, Pages: 104-112, ISSN: 1548-8403
Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. This paper investigates repeated security games with unknown (to the defender) game payoffs and attacker behaviors. As existing work assumes prior knowledge about either the game payoffs or the attacker's behaviors, they are not suitable for tackling our problem. Given this, we propose the first efficient defender strategy, based on an adversarial online learning framework, that can provably achieve good performance guarantees without any prior knowledge. In particular, we prove that our algorithm can achieve low performance loss against the best fixed strategy on hindsight (i.e., having full knowledge of the attacker's moves). In addition, we prove that our algorithm can achieve an efficient competitive ratio against the optimal adaptive defender strategy. We also show that for zero-sum security games, our algorithm achieves efficient results in approximating a number of solution concepts, such as algorithmic equilibria and the minimax value. Finally, our extensive numerical results demonstrate that, without having any prior information, our algorithm still achieves good performance, compared to state-of-the-art algorithms from the literature on security games, such as SUQR , which require significant amount of prior knowledge.
Zenonos A, Stein S, Jennings NR, 2016, An algorithm to coordinate measurements using stochastic human mobility patterns in large-scale participatory sensing settings, Pages: 3936-3942
© 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Bestmatch) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns.We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.
Zhao D, Ramchurn SD, Jennings NR, 2016, Fault tolerant mechanism design for general task allocation, Pages: 323-331, ISSN: 1548-8403
Copyright © 2016, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. We study a general task allocation problem, involving multiple agents that collaboratively accomplish tasks and where agents may fail to successfully complete the tasks assigned to them (known as execution uncertainty). The goal is to choose an allocation that maximises social welfare while taking their execution uncertainty into account (i.e., fault tolerant). To achieve this, we show that the post-execution verification (PEV)-based mechanism presented by Porter et al. (2008) is applicable if and only if agents' valuations are risk-neutral (i.e., the solution is almost universal). We then consider a more advanced setting where an agent's execution uncertainty is not completely predictable by the agent alone but aggregated from all agents' private opinions (known as trust). We show that PEV-based mechanism with trust is still applicable if and only if the trust aggregation is multilinear. Given this characterisation, we further demonstrate how this mechanism can be successfully applied in a real-world setting. Finally, we draw the parallels between our results and the literature of efficient mechanism design with general interdependent valuations.
Alan AT, Costanza E, Ramchurn S, et al., 2015, Managing energy tariffs with agents: a field study of a future smart energy system at home, Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers
Ghosh S, Reece S, Rogers A, et al., 2015, Modeling the Thermal Dynamics of Buildings: A Latent-Force-Model-Based Approach, ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, Vol: 6, ISSN: 2157-6904
Han TA, Tran-Thanh L, Jennings NR, 2015, The cost of interference in evolving multiagent systems, Pages: 1719-1720, ISSN: 1548-8403
Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. We study the situation of a decision-maker who aims to encourage the players of an evolutionary game theoretic system to follow certain desired behaviours. To do so, she can interfere in the system to reward her preferred behavioural patterns. However, this action requires certain cost (e.g., resource consumption). Given this, her main goal is to maintain an efficient trade-off between achieving the desired system status and minimising the total cost spent. Our results reveal interesting observations, which suggest that further investigations in the future are required.
Obraztsova S, Markakis E, Polukarov M, et al., 2015, On the convergence of iterative voting: how restrictive should restricted dynamics be?, AAAI 2015: Twenty-Ninth AAAI Conference on Artificial Intelligence, Pages: 993-999
We study convergence properties of iterative voting procedures. Such procedures are defined by a voting rule and a (restricted) iterative process, where at each step one agent can modify his vote towards a better outcome for himself. It is already known that if the iteration dynamics (the manner in which voters are allowed to modify their votes) are unrestricted, then the voting process may not converge. For most common voting rules this may be observed even under the best response dynamics limitation. It is therefore important to investigate whether and which natural restrictions on the dynamics of iterative voting procedures can guarantee convergence. To this end, we provide two general conditions on the dynamics based on iterative myopic improvements, each of which is sufficient for convergence. We then identify several classes of voting rules (including Positional Scoring Rules, Maximin, Copeland and Bucklin), along with their corresponding iterative processes, for which at least one of these conditions hold.
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