630 results found
Jennings N, PANAGOPOULOS AA, MALEKI S, et al., Advanced Economic Control of Electricity-based Space Heating Systems in Domestic Coalitions with Shared Intermittent Energy Resources, ACM Transactions on Intelligent Systems and Technology, ISSN: 2157-6912
Over the past few years, domestic heating automation systems (DHASs) that optimize the domestic space heating con-trol process with minimum user-input, utilizing appropriate occupancy prediction technology, have emerged as commercialproducts (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 windturbine generators. Now, in many regions of the world, such houses can sell energy to the grid but at a lower price than theprice of buying it. In this context, and given the anticipated increase in electrification of heating, the next generation DHASsneed to incorporate advanced economic control (AEC). Such AEC can exploit the energy buffer that heat in gloads 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 thefirst time, incorporates AEC. Our work is applicable to both individual houses and domestic coalitions and comes completewith an allocation mechanism to share the coalition gains. Importantly, we propose an effective heuristic heating scheduleplanning approach for collective AEC which: (i) has a complexity that scales in a linear and parallelizable manner with thecoalition size,and (ii) enables AdaHeat+ to handle the distinct preferences, in balancing heating cost and thermal discomfort,of the households. Our approach relies on stochastic
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.
Pruna RT, Polukarov M, Jennings NR, 2017, An asset pricing model with loss aversion and its stylized facts
© 2016 IEEE.A well-defined agent-based model able to match the widely observed properties of financial assets is valuable for testing the implications of various empirically observed heuristics associated with investors behaviour. In this paper, we extend one of the most successful models in capturing the observed behaviour of traders, and present a new behavioural asset pricing model with heterogeneous agents. Specifically, we introduce a new behavioural bias in the model, loss aversion, and show that it causes a major difference in the agents interactions. As we demonstrate, the resulting dynamics achieve one of the major objectives of the field, replicating a rich set of the stylized facts of financial data. In particular, for the first time our model enables us to match the following empirically observed properties: conditional heavy tails of returns, gains/loss asymmetry, volume power-law and long memory and volume-volatility relations.
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
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
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, , 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.
Panagopoulos AA, Alam M, Rogers A, et al., 2015, AdaHeat: A general adaptive intelligent agent for domestic heating control, 14th International Conference on Autonomous Agents and Multi-Agent Systems, Pages: 1295-1303
Panagopoulos AA, Chalkiadakis G, Jennings RN, 2015, Towards optimal solar tracking: a dynamic programming approach, AAAI-2015: 29th AAAI Conference on Artificial Intelligence, Pages: 695-701
The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from online providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.
Polukarov M, Obraztsova S, Rabinovich Z, et al., 2015, Convergence to Equilibria in Strategic Candidacy, International Joint Conference on Artificial Intelligence (IJCAI 2015), Pages: 624-630
Ramchurn S, Simpson E, Fischer J, et al., 2015, HAC-ER: A disaster response system based on human-agent collectives, 14th International Conference on Autonomous Agents and Multi-Agent Systems, Pages: 533-541
Ramchurn SD, Huynh TD, Ikuno Y, et al., 2015, HAC-ER: A disaster response system based on human-agent collectives (demonstration), Pages: 1921-1922, ISSN: 1548-8403
Simpson E, Venanzi M, Reece S, et al., 2015, Language understanding in the wild: Combining crowdsourcing and machine learning, Pages: 992-1002
Social media has led to the democratisation of opinion shar-ing. A wealth of information about public opinions, cur-rent events, and authors' insights into specific topics can be gained by understanding the text written by users. How-ever, there is a wide variation in the language used by different authors in different contexts on the web. This diversity in language makes interpretation an extremely challenging task. Crowdsourcing presents an opportunity to interpret the sentiment, or topic, of free-Text. However, the subjec-tivity and bias of human interpreters raise challenges in in-ferring the semantics expressed by the text. To overcome this problem, we present a novel Bayesian approach to lan-guage understanding that relies on aggregated crowdsourced judgements. Our model encodes the relationships between labels and text features in documents, such as tweets, web articles, and blog posts, accounting for the varying reliability of human labellers. It allows inference of annotations that scales to arbitrarily large pools of documents. Our evalu-ation using two challenging crowdsourcing datasets shows that by efficiently exploiting language models learnt from aggregated crowdsourced labels, we can provide up to 25% improved classifications when only a small portion, less than 4% of documents has been labelled. Compared to the six state-of-The-Art methods, we reduce by up to 67% the num-ber of crowd responses required to achieve comparable accu-racy. Our method was a joint winner of the CrowdFlower-CrowdScale 2013 Shared Task challenge at the conference on Human Computation and Crowdsourcing (HCOMP 2013).
Teacy WTL, Julier S, Nardi RD, et al., 2015, Observation modelling for vision-based target search by unmanned aerial vehicles, 14th International Conference on Autonomous Agents and Multi-Agent Systems, Pages: 1607-1614
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