630 results found
Alkayal ES, Jennings NR, Abulkhair MF, 2017, Efficient Task Scheduling Multi-Objective Particle Swarm Optimization in Cloud Computing, Pages: 17-24
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
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
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
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
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
Zhao D, Ramchurn SD, Jennings NR, 2016, Fault tolerant mechanism design for general task allocation, Pages: 323-331, ISSN: 1548-8403
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
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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