628 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
Jennings N, Pruna R, Polukarov M, An Asset Pricing Model with Loss Aversion and its Stylized Facts, Proc. IEEE Sym. on Computational Intelligence for Financial Engineering and Economics, Publisher: IEEE
A well-defined agent-based model able to match thewidely observed properties of financial assets is valuable fortesting the implications of various empirically observed heuristicsassociated with investors behaviour. In this paper, we extend oneof the most successful models in capturing the observed behaviourof traders, and present a new behavioural asset pricing modelwith heterogeneous agents. Specifically, we introduce a new be-havioural bias in the model, loss aversion, and show that it causesa major difference in the agents interactions. As we demonstrate,the resulting dynamics achieve one of the major objectives of thefield, replicating a rich set of the stylized facts of financial data.In particular, for the first time our model enables us to matchthe following empirically observed properties: conditional heavytails of returns, gains/loss asymmetry, volume power-law and longmemory 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 WLT, Rogers A, et al., 2016, Online planning for collaborative search and rescue by heterogeneous robot teams, Proc. 15th Int. Conf. on Autonomous Agents and Multi-Agent Systems, Publisher: ACM, Pages: 1024-1033
Collaboration is essential for effective performance by groupsof robots in disaster response settings. Here we are particularlyinterested in heterogeneous robots that collaborate incomplex scenarios with incomplete, dynamically changinginformation. In detail, we consider a search and rescue setting,where robots with different capabilities work togetherto accomplish tasks (rescue) and find information about furthertasks (search) at the same time. The state of the artfor such collaboration is robot control based on independentplanning for robots with different capabilities and typicallyincorporates uncertainty with only a limited scope. In contrast,in this paper, we create a joint plan to optimise allrobots’ actions incorporating uncertainty about the futureinformation gain of the robots. We evaluate our planner’sperformance in settings based on real disasters and find thatour approach decreases the response time by 20-25% comparedto state-of-the-art approaches. In addition, practicalconstraints 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 E, Nedea A, et al., 2016, Bid2Charge: Market user interface design for electric vehicle charging, Proc. 15th Int. Conf. on Autonomous Agents and Multi-Agent Systems, Publisher: ACM, Pages: 882-890
We consider settings where owners of electric vehicles (EVs) participatein a market mechanism to charge their vehicles. Existingwork on such mechanisms has typically assumed that participantsare fully rational and can report their preferences accurately to themechanism or to a software agent participating on their behalf.However, this may not be reasonable in settings with non-experthuman end-users. To explore this, we compare a fully expressiveinterface that covers the entire space of preferences to two restrictedinterfaces that reduce the space of possible options. To enable thisanalysis, we develop a novel game that replicates key features ofan abstract EV charging scenario. In two extensive evaluationswith over 300 users, we show that restricting the users’ preferencessignificantly reduces the time they spend deliberating. More surprisingly,it also leads to an increase in their utility compared tothe fully expressive interface (up to 70%). Finally, we find that areinforcement learning agent displays similar performance trends,enabling a novel methodology for evaluating market interfaces.
Tran-Thanh L, Xu H, Jennings NR, 2016, Playing repeated security games with no prior knowledge, 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), Publisher: ACM, Pages: 104-112
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.
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
© 2016 AI Access Foundation. All rights reserved.Many aspects of the design of efficient crowdsourcing processes, such as defining worker's bonuses, fair prices and time limits of the tasks, involve knowledge of the likely duration of the task at hand. In this work we introduce a new time-sensitive Bayesian aggregation method that simultaneously estimates a task's duration and obtains reliable aggregations of crowdsourced judgments. Our method, called BCCTime, uses latent variables to represent the uncertainty about the workers' completion time, the tasks' duration and the workers' accuracy. To relate the quality of a judgment to the time a worker spends on a task, our model assumes that each task is completed within a latent time window within which all workers with a propensity to genuinely attempt the labelling task (i.e., no spammers) are expected to submit their judgments. In contrast, workers with a lower propensity to valid labelling, such as spammers, bots or lazy labellers, are assumed to perform tasks considerably faster or slower than the time required by normal workers. Specifically, we use efficient message-passing Bayesian inference to learn approximate posterior probabilities of (i) the confusion matrix of each worker, (ii) the propensity to valid labelling of each worker, (iii) the unbiased duration of each task and (iv) the true label of each task. Using two realworld public datasets for entity linking tasks, we show that BCCTime produces up to 11% more accurate classifications and up to 100% more informative estimates of a task's duration compared to state-of-the-art methods.
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 S, Jennings N, 2016, Fault tolerant mechanism design for general task allocation, The 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016), Publisher: International Foundation for Autonomous Agents and Multiagent Systems
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
Tran-Thanh L, Huynh TD, Rosenfeld A, et al., 2015, Crowdsourcing Complex Workflows under Budget Constraints, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Publisher: AAAI Press, Pages: 1298-1304
We consider the problem of task allocation in crowdsourcing systems with multiple complex workflows, each of which consists of a set of interdependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a budget constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then determines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the corresponding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper.
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