658 results found
Jennings N, Zhang Y, Guo Q, et al., Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information, Proc 33rd AAAI Conf. on AI
Khan MM, Long T-T, Ramchurn SD, et al., 2018, Speeding Up GDL-Based Message Passing Algorithms for Large-Scale DCOPs, COMPUTER JOURNAL, Vol: 61, Pages: 1639-1666, ISSN: 0010-4620
Robu V, Vinyals M, Rogers A, et al., 2018, Efficient buyer groups with prediction-of-use electricity tariffs, IEEE Transactions on Smart Grid, Vol: 9, Pages: 4468-4479, ISSN: 1949-3061
Current electricity tariffs do not reflect the real costs that a customer incurs to a supplier, as units are charged at the same rate, regardless of the consumption pattern. In this paper, we propose a prediction-of-use (POU) tariff that better reflects the predictability cost of a customer. Our tariff asks customers to pre-commit to a baseline consumption, and charges them based on both their actual consumption and the deviation from the anticipated baseline. First, we study, from a cooperative game theory perspective, the cost game induced by a single such tariff, and show customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. Second, we study the efficient (i.e., cost-minimizing) structure of buying groups for the more realistic setting when multiple , competing POU tariffs are available. We propose a polynomial time algorithm to compute the efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic consumers in the U.K.
Stein S, Eshghi S, Maghsudi S, et al., 2018, Influence maximisation beyond organisational boundaries, Pages: 1-6
© 2017 IEEE. We consider the problem of choosing influential members within a social network, in order to disseminate a message as widely as possible. While this so-called problem of influence maximisation has been widely studied, little work considers partially-observable networks, where only part of a network is visible to the decision maker. Yet, this is critical in many applications, where an organisation needs to distribute its message far beyond its boundaries and beyond its usual sphere of influence. In this paper, we show that existing algorithms are not sufficient to handle such scenarios. To address this, we propose a set of novel adaptive algorithms that perform well in partially observable settings, achieving an up to 18% improvement on the non-Adaptive state of the art.
© 2018 Copyright is held by the owner/author(s). The Internet of Things (IoT) promises to enable applications that foster a more efficient, sustainable, and healthy way of life. If end-users are to take full advantage of these developments we foresee the need for future IoT systems and services to include an element of autonomy and support the delegation of agency to software processes and connected devices. To inform the design of such future technology, we report on a breaching experiment designed to investigate how people integrate an unpredictable service, through the veg box scheme, in everyday life. Findings from our semi-structured interviews and a two-week diary study with 11 households reveal that agency delegation must be warranted, that it must be possible to incorporate delegated decisions into everyday activities, and that delegation is subject to constraint. We further discuss design implications on the need to support people's diverse values, and their coordinative and creative practices.
Zenonos A, Stein S, Jennings NR, 2018, Coordinating measurements for environmental monitoring in uncertain participatory sensing settings, The Journal of Artificial Intelligence Research, Vol: 61, Pages: 433-474, ISSN: 1076-9757
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.
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
Haot D, Jennings NR, 2018, Selling multiple items via social networks, Pages: 68-76, ISSN: 1548-8403
© 2018 International Foundation for Autonomous Agents and Multiagent Systems. We consider a market where a seller sells multiple units of a comm odity in a social network. Each node/buyer In the social network can only directly communicate with her neighbours. i.e. the seller can only sell the commodity to her neighbours if she could not find a way to inform other buyers. In this paper, we design a novel prom otion mechanism that incentivizes all buyers, who are aware of the sale, to invite all their neighbours to join the sale, even though there is no guarantee that their efforts will be paid. While tradit ional sale promotions such as sponsored search auctions cannot guarantee a positive return for the advertiser (the seUer), our mecha nism guarantees that the seller's revenue is better than not using the advertising. More importantly, the seller does not need to pay if the advertising is not beneficial to her.
Manino E, Tran-Thanh L, Jennings NR, 2018, On the efficiency of data collection for crowdsourced classification, Pages: 1568-1575, ISSN: 1045-0823
© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. The quality of crowdsourced data is often highly variable. For this reason, it is common to collect redundant data and use statistical methods to aggregate it. Empirical studies show that the policies we use to collect such data have a strong impact on the accuracy of the system. However, there is little theoretical understanding of this phenomenon. In this paper we provide the first theoretical explanation of the accuracy gap between the most popular collection policies: the non-adaptive uniform allocation, and the adaptive uncertainty sampling and information gain maximisation. To do so, we propose a novel representation of the collection process in terms of random walks. Then, we use this tool to derive lower and upper bounds on the accuracy of the policies. With these bounds, we are able to quantify the advantage that the two adaptive policies have over the non-adaptive one for the first time.
, 2018, A differential privacy mechanism with network effects for crowdsourcing systems, Pages: 1998-2000, ISSN: 1548-8403
© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. In crowdsourcing systems, it is important for the crowdsource campaign initiator to incentivize users to share their data to produce results of the desired computational accuracy. This problem becomes especially challenging when users are concerned about the privacy of their data. To overcome this challenge, existing work often aims to provide users with differential privacy guarantees to incentivize privacy-sensitive users to share their data. However, this work neglects the network effect that a user enjoys greater privacy protection when he aligns his participation behaviour with that of other users. To explore the network effect and provide a suitable differential privacy guarantee, we design PINE (Privacy Incentivization with Network Effects). PLNE is a mechanism that maximizes the initiator's payoff while providing participating users with privacy protections.
, 2018, Adaptive incentive selection for crowdsourcing contests, Pages: 2100-2102, ISSN: 1548-8403
Mosaddek Khan MD, Yeoh W, Tran-Thanh L, et al., 2018, A near-optimal node-to-agent mapping heuristic for GDL-based DCOP algorithms in multi-agent systems, Pages: 1613-1621, ISSN: 1548-8403
© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. Distributed Constraint Optimization Problems (DCOPs) can be used to model a number of multi-agent coordination problems. The conventional DCOP model assumes that the subproblem that each agent is responsible for (i.e. the mapping of nodes in the constraint graph to agents) is part of the model description. While this assumption is often reasonable, there are many applications where there is some flexibility in making this assignment. In this paper, we focus on this gap and make the following contributions: (1) We formulate this problem as an optimization problem, where the goal is to find an assignment that minimizes the completion time of the DCOP algorithm (e.g. Action-GDL or Max-Sum) that operates on this mapping. (2) We propose a novel heuristic, called MNA, that can be executed in a centralized or decentralized manner. (3) Our empirical evaluation illustrates a substantial reduction in completion time, ranging from 16% to 40%, without affecting the solution quality of the algorithms, compared to the current state of the art. In addition, we observe empirically that the completion time obtained from our approach is near-optimal; it never exceeds more than 10% of what can be achieved from the optimal node-to-agent mapping.
, 2018, A generic domain pruning technique for GDL-based DCOP algorithms in cooperative multi-agent systems, Pages: 1595-1603, ISSN: 1548-8403
© 2018 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. Generalized Distributive Law (GDL) based message passing algorithms, such as Max-Sum and Bounded Max-Sum, are often used to solve distributed constraint optimization problems in cooperative multi-agent systems (MAS). However, scalability becomes a challenge when these algorithms have to deal with constraint functions with high arity or variables with a large domain size. In either case, the ensuing exponential growth of search space can make such algorithms computationally infeasible in practice. To address this issue, we develop a generic domain pruning technique that enables these algorithms to be effectively applied to larger and more complex problems. We theoretically prove that the pruned search space obtained by our approach does not affect the outcome of the algorithms. Moreover, our empirical evaluation illustrates a significant reduction of the search space, ranging from 33% to 81%, without affecting the solution quality of the algorithms, compared to the state-of-the-art.
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.
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
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
, 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.
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
, 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%.
, 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.
, 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.
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
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
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
Jennings NR, 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.
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