649 results found
Alkayal ES, Jennings NR, Abulkhair MF, 2018, Survey of task scheduling in cloud computing based on particle swarm optimization, Pages: 1-6
© 2017 IEEE. Particle Swarm Optimization (PSO) is a metaheuristic algorithm applied to optimize many cloud-computing problems. Task scheduling is a critical problem in the cloud computing that needs to be optimized. Several research studies have been conducted to improve cloud-computing task scheduling using PSO algorithms. This paper analyzes this work in terms of its objectives and orientation. Finally, it identifies the key areas that require further research.
Pruna RT, Polukarov M, Jennings NR, 2018, Avoiding regret in an agent-based asset pricing model, FINANCE RESEARCH LETTERS, Vol: 24, Pages: 273-277, ISSN: 1544-6123
Zenonos A, Stein S, Jennings NR, 2018, Coordinating measurements for environmental monitoring in uncertain participatory sensing settings, Journal of Artificial Intelligence Research, Vol: 61, Pages: 433-474
© 2018 AI Access Foundation. All rights reserved. Environmental monitoring allows authorities to understand the impact of potentially harmful phenomena, such as air pollution, excessive noise and radiation. Recently, there has been considerable interest in participatory sensing as a paradigm for such large-scale data collection because it is cost-effective and able to capture more fine-grained data than traditional approaches that use stationary sensors scattered in cities. In this approach, ordinary citizens (non-expert contributors) collect environmental data using low-cost mobile devices. However, these participants are generally self-interested actors that have their own goals and make local decisions about when and where to take measurements. This can lead to highly ineffcient outcomes, where observations are either taken redundantly or do not provide sufficient information about key areas of interest. To address these challenges, it is necessary to guide and to coordinate participants, so they take measurements when it is most informative. To this end, we develop a computationally-effcient coordination algorithm (adaptive Best-Match) that suggests to users when and where to take measurements. Our algorithm exploits probabilistic knowledge of human mobility patterns, but explicitly considers the uncertainty of these patterns and the potential unwillingness of people to take measurements when requested to do so. In particular, our algorithm uses a local search technique, clustering and random simulations to map participants to measurements that need to be taken in space and time. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the current state of the art by up to 24% in terms of utility gained.
Alkayal ES, Jennings NR, Abulkhair MF, 2017, Automated Negotiation using Parallel Particle Swarm Optimization for Cloud Computing Applications, International Conference on Computer and Applications (ICCA), Publisher: IEEE, Pages: 26-35
Alkayal ES, Jennings NR, Abulkhair MF, 2017, Survey of Task Scheduling in Cloud Computing based on Particle Swarm Optimization, International Conference on Electrical and Computing Technologies and Applications (ICECTA), Publisher: IEEE, Pages: 263-268
An B, Jennings N, Li ZJ, 2017, ACM TIST Special Issue on Urban Intelligence, ACM Transactions on Intelligent Systems and Technology, Vol: 9, ISSN: 2157-6904
Augustin A, Venanzi M, Rogers A, et al., 2017, Bayesian aggregation of categorical distributions with applications in crowdsourcing, Pages: 1411-1417, ISSN: 1045-0823
A key problem in crowdsourcing is the aggregation of judgments of proportions. For example, workers might be presented with a news article or an image, and be asked to identify the proportion of each topic, sentiment, object, or colour present in it. These varying judgments then need to be aggregated to form a consensus view of the document's or image's contents. Often, however, these judgments are skewed by workers who provide judgments randomly. Such spammers make the cost of acquiring judgments more expensive and degrade the accuracy of the aggregation. For such cases, we provide a new Bayesian framework for aggregating these responses (expressed in the form of categorical distributions) that for the first time accounts for spammers. We elicit 796 judgments about proportions of objects and colours in images. Experimental results show comparable aggregation accuracy when 60% of the workers are spammers, as other state of the art approaches do when there are no spammers.
Beck Z, Luke Teacy WT, Rogers A, et al., 2017, Collaborative online planning for automated victim search in disaster response, Robotics and Autonomous Systems, Vol: 100, Pages: 251-266, ISSN: 0921-8890
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 an automated victim search setting, where unmanned aerial vehicles (UAVs) with different capabilities work together to scan for mobile phones and find and provide information about possible victims near these phone locations. The state of the art for such collaboration is robot control based on independent planning for robots with different tasks and typically incorporates uncertainty with only a limited scope. In contrast, in this paper, we take into account complex relations between robots with different tasks. As a result, we create a joint, full-horizon plan for the whole robot team by optimising over the uncertainty of future information gain using an online planner with hindsight optimisation. This joint plan is also used for further optimisation of individual UAV paths based on the long-term plans of all robots. We evaluate our planner’s performance in a realistic simulation environment based on a real disaster and find that our approach finds victims 25% faster compared to current state-of-the-art approaches.
Jennings N, Zenonos A, Stein S, 2017, A trust-based coordination system for participatory sensing applications, 5th Int. Conf. on Human Computation and Crowdsourcing, Publisher: AAAI, Pages: 226-234
Participatory sensing (PS) has gained significant attention asa crowdsourcing methodology that allows ordinary citizens(non-expert contributors) to collect data using low-cost mobiledevices. In particular, it has been useful in the collectionof environmental data. However, current PS applicationssuffer from two problems. First, they do not coordinate themeasurements taken by their users, which is required to maximisesystem efficiency. Second, they are vulnerable to maliciousbehaviour. In this context, we propose a novel algorithmthat simultaneously addresses both of these problems. Specifically,we use heteroskedastic Gaussian Processes to incorporateusers’ trustworthiness into a Bayesian spatio-temporalregression model. The model is trained with measurementstaken by participants, thus it is able to estimate the value ofthe phenomenon at any spatio-temporal location of interestand also learn the level of trustworthiness of each user. Giventhis model, the coordination system is able to make informeddecisions concerning when, where and who should take measurementsover a period of time. We empirically evaluate ouralgorithm on a real-world human mobility and air qualitydataset, where malicious behaviour is synthetically produced,and show that our algorithm outperforms the current state ofthe art by up to 60.4% in terms of RMSE while having a reasonableruntime.
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
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
Stein S, Eshghi S, Maghsudi S, et al., 2017, Heuristic algorithms for influence maximization in partially observable social networks, Pages: 20-32, ISSN: 1613-0073
Copyright © 2017 for the individual papers by the papers' authors. We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible. This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this preliminary study, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network. We show that these algorithms outperform the state of the art by up to 38% in networks with partial observability.
Stein S, Gerding EH, Nedea A, et al., 2017, Evaluating market user interfaces for electric vehicle charging using Bid2Charge, Pages: 4939-4943, ISSN: 1045-0823
We consider settings where electric vehicle drivers participate in a market mechanism to charge their vehicles. Existing work typically assumes that participants are fully rational and can report their charging preferences accurately. However, this may not be reasonable in settings with non-experts. To explore this, we design a novel game called Bid2Charge and compare a fully expressive interface that covers the entire space of preferences to two restricted interfaces that offer fewer possible reports. We show that restricting the users' preferences significantly reduces deliberation times while also leading to an increase in utility by up to 70%.
Waniek M, Tran-Thanh L, Michalak TP, et al., 2017, The dollar auction with spiteful players, Pages: 736-742
Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The dollar auction is an auction model used to analyse the dynamics of conflict escalation. In this paper, we analyse the course of an auction when participating players are spiteful, i.e., they are motivated not only by their own profit, but also by the desire to hurt the opponent. We investigate this model for the complete information setting, both for the standard scenario and for the situation where auction starts with nonzero bids. Our results give us insight into the possible effects of meanness onto conflict escalation.
Zhang Y, An B, Tran-Thanh L, et al., 2017, Optimal escape interdiction on transportation networks, Pages: 3936-3944, ISSN: 1045-0823
Preventing crimes or terrorist attacks in urban areas is challenging. Law enforcement officers need to respond quickly to catch the attacker on his escape route, which is subject to time-dependent traffic conditions on transportation networks. The attacker can strategically choose his escape path and driving speed to avoid being captured. Existing work on security resource allocation has not considered such scenarios with time-dependent strategies for both players. Therefore, in this paper, we study the problem of efficiently scheduling security resources for interdicting the escaping attacker. We propose: 1) a new defender-attacker security game model for escape interdiction on transportation networks; and 2) an efficient double oracle algorithm to compute the optimal defender strategy, which combines mixed-integer linear programming formulations for best response problems and effective approximation algorithms for improving the scalability of the algorithms. Experimental evaluation shows that our approach significantly outperforms baselines in solution quality and scales up to realistic-sized transportation networks with hundreds of intersections.
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.
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