Abstract: Today’s crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowdsourcing of the software development processes, the system needs to enact coordination mechanisms which integrate human creativity with machine support. While workflows can be used to handle highly-structured and predictable labour processes, they are less suitable for software development methodologies where unpredictability is an unavoidable part the process. This is especially true in phases of requirement elicitation and feature development, when both the client and development communities change with time. In this paper we present models and techniques for coordination of human workers in crowdsourced software development environments. The techniques augment the existing Social Compute Unit (SCU) concept-a general framework for management of ad-hoc human worker teams-with versatile coordination protocols expressed in the Lightweight Social Calculus (LSC). This approach allows us to combine coordination and quality constraints with dynamic assessments of software-user’s desires, while dynamically choosing appropriate software development coordination models.

Citation: Dave Murray-Rust, Ognjen Scekic, Petros Papapanagiotou, Hong-Linh Truong, Dave Robertson, Schahram Dustdar: A Collaboration Model for Community-Based Software Development with Social Machines. EAI Endorsed Trans. Collaborative Computing 1(5): e6 (2015).

Download: http://bit.ly/2iMIuNU

Abstract: Novel types of crowdsourcing systems require a wider spectrum of incentives for efficient motivation and management of human workers taking part in complex collaborations. Incentive management techniques used in conventional crowdsourcing platforms are not suitable for more intellectually-challenging tasks. Currently, incentives are custom-developed and managed by each particular platform. This prevents incentive portability and cross-platform comparison. In this paper we present PRINGL – a domain-specific language for programming and managing complex incentive strategies for socio-technical platforms in general. It promotes re-use of proven incentive logic and simplifies modeling, adjustment and enactment of complex incentives for socio-technical systems. We demonstrate its applicability and expressiveness on a set of realistic use-cases and discuss its properties.

Citation: Ognjen Scekic, Hong Linh Truong, Schahram Dustdar: PRINGL – A domain-specific language for incentive management in crowdsourcing. Computer Networks 90: 14-33(2015).

Download: http://bit.ly/2iMHw4e

Abstract: Computer interfaces provide an environment that allows for multiple objectively optimal solutions but individuals will, over time, use a smaller number of subjectively optimal solutions, developed as habits that have been formed and tuned by repetition. Designing an interface agent to provide assistance in this environment thus requires not only knowledge of the objectively optimal solutions, but also recognition that users act from habit and that adaptation to an individual’s subjectively optimal solutions is required. We present a dynamic Bayesian network model for predicting a user’s actions by inferring whether a decision is being made by deliberation or through habit. The model adapts to individuals in a principled manner by incorporating observed actions using Bayesian probabilistic techniques. We demonstrate the model’s effectiveness using specific implementations of deliberation and habitual decision making, that are simple enough to transparently expose the mechanisms of our estimation procedure. We show that this implementation achieves > 90% prediction accuracy in a task with a large number of optimal solutions and a high degree of freedom in selecting actions.

Citation: A.H. Cruickshank, R. Shillcock, S. Ramamoorthy, Predicting actions using an adaptive probabilistic model of human decision behaviours, Poster, In Ext. Proc. Conference on User Modelling, Adaptation and Personalization (UMAP), 2015.

Download: http://bit.ly/2joIdRU

Abstract: The key for effective interaction in many multiagent applications is to reason explicitly about the behaviour of other agents, in the form of a hypothesised behaviour. While there exist several methods for the construction of a behavioural hypothesis, there is currently no universal theory which would allow an agent to contemplate the correctness of a hypothesis. In this work, we present a novel algorithm which decides this question in the form of a frequentist hypothesis test. The algorithm allows for multiple metrics in the construction of the test statistic and learns its distribution during the interaction process, with asymptotic correctness guarantees. We present results from a comprehensive set of experiments, demonstrating that the algorithm achieves high accuracy and scalability at low computational costs.

Citation: S. Albrecht, S. Ramamoorthy, Are you doing what I think you are doing? Criticising uncertain agent models, In Proc. Conference on Uncertainty in Artificial Intelligence (UAI), 2015.

Download: http://bit.ly/2jcLEOD

Abstract: Modern development of computing systems caters the collaboration of human-based resources together with machine-based resources as active compute units. Those units can be dynamically provisioned on-demand for solving complex tasks, such as observed in collaborative applications, crowd sourced applications, and human task workflows. Such collaborations involve very diverse compute units, which have different capabilities and reliability. While the reliability analysis for machine-based compute units has been widely developed, the reliability analysis for the hybrid human-machine collaborations has not been extensively studied. In this paper we present models and a framework for analyzing the reliability of hybrid compute units (HCU), which represent on-demand collectives of humans collaboration supported by machines (hardware and software units) for performing tasks. We present the implementation of our models and study the reliability of HCUs in a simulated system for infrastructure maintenance scenarios. Our evaluation shows that the proposed framework is effective for measuring the reliability of the collaboration collectives, and beneficial to obtain insights for improvements.

Citation: Candra, Muhammad ZC, Hong-Linh Truong, and Schahram Dustdar. “Analyzing Reliability in Hybrid Compute Units.” In: Collaboration and Internet Computing (CIC), IEEE Conference on, 2015.

Download: http://bit.ly/2iidadR

Abstract: Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.

Citation: S. Albrecht, J. Crandall, S. Ramamoorthy, An Empirical Study on the Practical Impact of Prior Beliefs over Policy Types, In Proc. AAAI Conference on Artificial Intelligence (AAAI), 2015.

Download: http://bit.ly/2jcOpzr

Abstract: Past research has studied two approaches to utilise pre-defined policy sets in repeated interactions: as experts, to dictate our own actions, and as types, to characterise the behaviour of other agents. In this work, we bring these complementary views together in the form of a novel meta-algorithm, called Expert-HBA (E-HBA), which can be applied to any expert algorithm that considers the average (or total) payoff an expert has yielded in the past. E-HBA gradually mixes the past payoff with a predicted future payoff, which is computed using the type-based characterisation. We present results from a comprehensive set of repeated matrix games, comparing the performance of several well-known expert algorithms with and without the aid of E-HBA. Our results show that E-HBA has the potential to significantly improve the performance of expert algorithms.

Citation: S. Albrecht, J. Crandall, S. Ramamoorthy, E-HBA: Using Action Policies for Expert Advice and Agent Typification, In Proc. AAAI-Workshop on Multiagent Interaction without Prior Coordination (MIPC), 2015.

Download: http://bit.ly/2j5V9MT

Abstract: Despite the fact that social computation systems involve interaction mechanisms that closely resemble well-known models of agent coordination, current applications in this area make little or no use of the techniques the agent-based systems literature has to offer. In order to bridge this gap, this paper proposes a data-driven method for defining and deploying agent interaction protocols that is entirely based on using the standard architecture of the World Wide Web. This obviates the need of bespoke message passing mechanisms and agent platforms, thereby facilitating the use of agent coordination principles in standard Web-based applications. We describe a prototypical implementation of the architecture and experimental results that prove it can deliver the scalability and robustness required of modern social computation applications while maintaining the expressiveness and versatility of agent interaction protocols.

Citation: M. Rovatsos, D. Diochnos, and M. Craciun. Agent Protocols for Social Computation. Advances in Social Computing and Multiagent Systems, CCIS 541, Springer, 2015.

Download: http://bit.ly/2jcIfiG

Abstract: Ridesharing, that is, the problem of finding parts of routes that can be shared by several travelers with different points of departure and destinations, is a complex, multiagent decision-making problem. The problem has been widely studied but only for the case of ridesharing using freely moving vehicles not bound to fixed routes and/or schedules—ridesharing on timetabled public transport services has not been previously considered. In this article, we address this problem and propose a solution employing strategic multiagent planning that guarantees that for any shared journey plan found, each individual is better off taking the shared ride rather than traveling alone, thus providing a clear incentive to participate in it. We evaluate the proposed solution on real-world scenarios in terms of the algorithm’s scalability and the ability to address the inherent trade-off between cost savings and the prolongation of journey duration. The results show that under a wide range of circumstances our algorithm finds attractive shared journey plans. In addition to serving as a basis for traveler-oriented ridesharing service, our system allows stakeholders to determine appropriate pricing policies to incentivize group travel and to predict the effects of potential service changes.

Citation: J. Hrncir, M. Rovatsos, and M. Jakob. Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach, Journal of Intelligent Transportation Systems, 19(1):89-105, 2015.

Download: http://bit.ly/2ipJ8mP

Abstract: We propose a novel approach for Ad-Hoc WiFi based distribution of information within large crowds of mobile users. The work is motivated by civil protection scenarios where infrastructure based communication often breaks down in cases of emergency. We follow a basic opportunistic networking approach by making use of the smartphones’ built-in WiFi hotspot functionality which in combination with the devices switching between access point and client modes facilitates the propagation of messages on a multi-hop basis. We make three contributions with respect to previous work on this topic. First, we empirically determine core boundary conditions given by the performance of modern smartphones. To maximize system performance under such circumstances we propose novel heuristics for a mode switching strategy based on client mobility instead of random strategies that have mainly been utilized so far. Finally, we compare its performance to a random role switching strategy in a large-scale simulation based on a real dataset consisting of movement traces from 28’000 people during a three day festival in Zurich. Within the simulation we investigate the influence of various parameters on the system’s behavior.

Citation: Franke, T., Negele, S., Kampis, G. and Lukowicz, P. (2015): Leveraging Human Mobility in Smartphone Based Ad-Hoc Information Distribution in Crowd Management Scenarios, submitted to MobiSys 2015.

Download: http://bit.ly/2jcHCpp

Abstract: Collaborative localization is a special case for knowledge fusion where information is exchanged in order to attain improved global and local knowledge. We propose analytical as well as agent based simulation models for pedestrian dead reckoning (PDR) systems in agents collaborating to improve their location estimate by exchanging subjective position information when two agents are detected close to each other. The basis of improvement is the fact that two agents are at approximately the same position when they meet, and this can be used to update local position information. In analytical models we find that the localization error remains asymptotically finite in infinite systems or when there is at least one immobile agent (i.e. an agent with a zero localization error) in the system. In the agent model we tested finite systems under realistic (that is, inexact) meeting conditions and tested localization errors as function of several parameters. We found that a large finite system comprising hundreds of users is capable of collaborative localization with an essentially constant error under various conditions. The presented models can be used for predicting the improvement in localization that can be achieved by a collaboration among several mobile computers. Besides, our results can be considered as first steps toward a more general collaborative (incremental) form of knowledge fusion.

Citation: Kampis, G., Kantelhardt, J.W, Kloch, K., and Lukowicz, P. (2014): Analytical and Simulation Models for Collaborative Localization, J. Computational Science 6 (2015) 1–10.

Download: http://bit.ly/2jcMvP3

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: There is increasing interest in the agent community to integrate the concept of emotions and artificial agents. The spectrum of available solutions reaches from applications and models of emotions to complete axiomatised logics. Despite the rich offer of solutions, available works neglect individual personality as a significant factor for the outcome of emotional behaviour pattern. However, different personalities affect all relevant phases of human decision-making processes. Hence, this paper introduces and discusses existing personality theories and highlights the fact that one of them is widely accepted in psychology and should be adopted by the agent-community. We integrate the characteristics of this personality theory into the life-cycle of BDI agents and discuss two different versions of the BDI algorithm – a naive one and one that balances the commitment between means and ends. The outlined algorithm is implemented as a prototype model in AntMe!, an agent-based simulation environment for behavioural studies. The experiments performed in this environment show that personality indeed affects all relevant phases of the decision-making process, laying the foundations for future empirical studies.

Citation: Sebastian Ahrndt, Johannes Fähndrich and Sahin Albayrak. Modelling of Personality in Agents: From Psychology to Implementation.

Download: http://bit.ly/1VzKTfE

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: We study simulation models of spreading on peer-to-peer communication networks where any peer (or agent) can be the source of information, be it sensory recognition or contextual knowledge. In such a situation the value or quality of information is of key relevance. Questions of trust, provenance and the problem of the interaction pattern arise and are approached by three different algorithms in our paper: (i) “quantitative democracy”, where knowledge is averaged on a meeting (ii) “experience takes all”, where the more experienced (the teacher) overwrites all prior knowledge of the less experienced (the “student”), and (iii) “transitive experience” where not only information but also experience is handed over. We compare these different regimes and identify their tradeoffs.

Keywords: Trust, provenance, self-organization, emergence, collaborative information processing.

Citation: George Kampis and Paul Lukowicz. Collaborative Activity Recognition.

Download: http://bit.ly/1SQJhad

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Trust is an important factor in any relationship and within teamwork is no exception. Teammates need to trust each other to achieve common tasks effectively and efficiently. Teamwork that combines both a human and an Intelligent Virtual Agent (IVA) has drawn much interest; nevertheless, the handling of trust between humans and IVAs is unclear. In this paper, we seek to understand how people trust an IVA teammate. The current study considers two facets of trust: personality and cognition. Our experimental study with 55 participants, involving a collaborative human-IVA task, sought to determine whether human trust in an IVA teammate is affected by the IVA’s personality and whether that differs when the IVA’s personality matches the human’s personality. Furthermore, we sought to understand the relative importance of personality-based versus cognitive-based facets (e.g. the information offered by the IVA) on human trust in the IVA and the resultant effect of human trust on team performance. Results indicated that cognitive-based facets played a more dominant role in establishing trust than personality-based facets. Additionally, the results showed that human trust in the IVA had a significantly positive influence on human-IVA team performance.

Keywords: Intelligent Virtual Agent, Human-Agent Teamwork, Multimodal Communication, Trust, Personality, FFM, Team Performance.

Citation: Nader Hanna and Deborah Richards. In the Agent we Trust! The Role of Personality and Cognition in Human Trust in Virtual Agents.

Download: http://bit.ly/23I9B1H

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Despite the prevalence of e-learning systems in schools, most of today’s systems do not personalize educational data to the individual needs of each student. This paper proposes a new algorithm for sequencing questions to students that is empirically shown to lead to better performance and engagement in real schools when compared to a baseline approach. It is based on using knowledge tracing to model students’ skill acquisition over time, and to select questions that advance the student’s learning within the range of the student’s capabilities, as determined by the model. The algorithm is based on a Bayesian Knowledge Tracing (BKT) model that incorporates partial credit scores, reasoning about multiple attempts to solve problems, and integrating item difficulty. This model is shown to outperform other BKT models that do not reason about (or reason about some but not all) of these features. The model was incorporated into a sequencing algorithm and deployed in two classes in different schools where it was compared to a baseline sequencing algorithm that was designed by pedagogical experts. In both classes, students using the BKT sequencing approach solved more difficult questions and attributed higher performance than did students who used the expert-based approach. Students were also more engaged using the BKT approach, as determined by their interaction time and number of log-ins to the system, as well as their reported opinion. We expect our approach to inform the design of better methods for sequencing and personalizing educational content to students that will meet their individual learning needs.

Citation: Yossi Ben David, Avi Segal, and Kobi Gal. Sequencing Educational Content in Classrooms using Bayesian Knowledge Tracing.

Download: http://bit.ly/23I8nDB

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: With the improvement in agent technology and agent capabilities we foresee increasing use of agents in social contexts and, in particular, in human-agent team applications. To be effective in such team contexts, agents need to understand and adapt to the expectation of human team members. This paper presents our study on how behavioral strategies of agents affect the humans’ trust in those agents and the concomitant performance expectations that follow in virtual team environments. We have developed a virtual teamwork problem that involves repeated interaction between a human and several agent types over multiple episodes. The domain involves transcribing spoken words, and was chosen so that no specialized knowledge beyond language expertise is required of the human participants. The problem requires humans and agents to independently choose subset of tasks to complete without consulting with the partner and utility obtained is a function of the payment for task, if completed, minus its efforts. We implemented several agents types, which vary in how much of the teamwork they perform over different interactions in an episode. Experiments were conducted with subjects recruited from the MTurk. We collected both teamwork performance data as well as surveys to gauge participants’ trust in their agent partners. We trained a regression model on collected game data to identify distinct behavioral traits. By integrating the prediction model of player’s task choice, a learning agent is constructed and shown to significantly improve both social welfare, by reducing redundant work without sacrificing task completion rate, as well as agent and human utilities.

Keywords: Human-agent interaction, teamwork, trust, adaptation.

Citation: Feyza Hafizoglu and Sandip Sen. Evaluating Trust Levels in Human-agent Teamwork in Virtual Environments.

Download: http://bit.ly/1S8ceCA

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Interactive agent-based systems are becoming increasingly ubiquitous in our everyday lives, helping people in many domains including healthcare, transportation, and energy. As such, there is a need to investigate how humans and agents interact with each other to maximize the benefit that such systems can offer. In this paper, we present a field study that lasted for six weeks, in which 12 different households were required to interact on a daily basis with an agent-based system in order to manage their electricity pricing scheme. The main goal of this study was to explore long term interactions between human users and an agent to understand how people’s trust towards an agent may change over time, and consequently affect their autonomy and interaction preferences. Our results suggest that flexible autonomy shows promise for sustaining users’ trust and engagement with an agent, despite its occasional mistakes.

Keywords: Human-Agent Interaction, Autonomous Agents, Flexible Autonomy, Energy.

Citation: Alper Alan, Enrico Costanza, Sarvapali Ramchurn, Joel Fischer, Tom Rodden and Nicholas Jennings. Evaluating Human-Agent Interaction in the Wild.

Download: http://bit.ly/1S8bZri

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Crowdsourcing has emerged as a powerful way to provide computer systems with quick and easy access to human intelligence. However, there is a risk that online crowd workers could be directed to perform harmful tasks. To understand the impact of financial incentives on paid crowd workers’ willingness to behave maliciously, we conducted a series of experiments in which we hired crowd workers via one crowdsourcing task (Attack task) to attack a different crowdsourcing task (Target task. We found that roughly one third of all crowd workers were willing to provide the attack task with potentially sensitive information from the target task, and that we could double this number by increasing the payment of the Attack task. Based on exit interviews and community feedback, we discuss some of what workers reported. Our findings reveal a measurable cost to completing malicious work that well-meaning task designers can leverage to protect their systems from attack.

Citation: Walter Lasecki, Jaime Teevan and Ece Kamar. What’s Your Price? The Cost of Asking Crowd Workers to Behave Maliciously.

Download: http://bit.ly/1VB1DTh

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Recently, there has been an increase of interest in domains involving repeated interactions between defenders and adversaries. This has been modeled as a repeated Stackelberg Security Game (repeated SSG). Although different behavioral models have been proposed for the attackers in these games, human subjects experiments for testing these behavioral models in repeated SSGs have not been conducted previously. This paper presents the first “longitudinal study” – at least in the context of SSGs – of testing human behavior models in repeated SSG settings. We provide the following contributions in this paper. First, in order to test the behavioral models, we design a game that simulates the repeated interactions between the defender and the adversary and deploy it on Amazon Mechanical Turk (AMT). Human subjects are asked to participate in this repeated task in rounds of the game, with a break between consecutive rounds. Second, we develop several approaches to keep the human subjects motivated throughout the course of this longitudinal study so that they participate in all measurement occasions, thereby minimizing attrition. We provide results showing improvements of retention rate due to implementation of these approaches. Third, we propose a way of choosing representative payoffs that fit the real-world scenarios as conducting these experiments are extremely time-consuming and we can only conduct a limited number of such experiments.

Keywords: Game Theory, Human Behavior Models, Repeated Stackelberg Games, Longitudinal Experiments, Amazon Mechanical Turk.

Citation: Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov and Milind Tambe. Conducting Longitudinal Experiments with Behavioral Models in Repeated Stackelberg Security Games on Amazon Mechanical Turk.

Download: http://bit.ly/1rf3wsu

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Humans use sketches drawn on paper, on a computer, or via hand gestures in the air as part of their communications. To recognize shapes in sketches, most existing work focuses on offline (post-drawing) recognition methods, trained on large sets of examples which serve as a plan library for the recognition method. These methods do not allow on-line recognition, and require a very large library (or expensive pre-processing) in order to recognize shapes that have been translated, rotated or scaled. Inspired by mirroring processes in human brains we present an online shape recognizer that identifies multi-stroke geometric shapes without a plan library. Instead, the recognizer uses a shape-drawing planner for drawn-shape recognition, i.e., a form of plan recognition by planning. This method (1) allows recognition of shapes that is immune to geometric translations, rotations, and scale; (2) eliminates the need for storing a library of shapes to be matched against drawings (instead, only needs a set of possible Goals and a planner that can instantiate them in any manner); and (3) allows fast on-line recognition. The method is particularly suited to complete agents, that must not only recognize sketches, but also produce them, and therefore necessarily have a drawing planner already. We compare the performance of different variants of the recognizer to that of humans, and show that its recognition level is close to that of humans, while making less recognition errors early in the recognition process.

Citation: Mor Vered and Gal Kaminka. If You Can Draw It, You Can Recognize It: Mirroring For Sketch Recognition.

Download: http://bit.ly/1WcL6UC

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Social interaction has essential effects on social reality, which can even outweigh the importance of the physical effects, but which are not directly nor objectively observable. Understanding the social contexts in which actions and interactions take place is thus of utmost importance for planning one’s goals and activities. We claim that joint action should be considered from both the social and the physical perspective jointly. These aspects are interdependent and influence each other continuously. In order to support the inclusion of social aspects into state descriptions for joint actions, we propose a methodological approach for social analysis of joint action, that enables to identify and represent the social characteristics of a joint action setting. Through the use of social practices it is possible to combine both physical and social aspects of joint actions. We show how these social practices can be used to design agents and robots that take into account both the social and physical context and goals.

Citation: Virginia Dignum, Frank Dignum and Catholijn Jonker. Social Aspects of Joint Actions: from analysis to design of social actions.

Download: http://bit.ly/22KPwCa

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: It can be hard to automatically identify sensitive content in images or other media because significant context is often necessary to interpret noisy content and complex notions of sensitivity. Online crowds can help computers interpret information that cannot be understood algorithmically. However, systems that use this approach can unwittingly show workers information that should remain private. For instance, images sent to the crowd may accidentally include faces or geographic identifiers in the background, and information pertaining to a task (e.g., the amount of a bill) may appear alongside private information (e.g., an account number). This paper introduces an approach for using crowds to filter information from sensory data that should remain private, while retaining information needed to complete a specified task. The pyramid workflow that we introduce allows crowd workers to identify private information while never having complete access to the (potentially private) information they are filtering. Our approach is flexible, easily configurable, and can protect user information in settings where automated approaches fail. Our experiments with 4685 crowd workers show that it performs significantly better than previous approaches.

Citation: Walter Lasecki, Mitchell Gordon, Jaime Teevan, Ece Kamar and Jeffrey Bigham. CrowdMask: Privacy-Preserving Crowd-Powered Systems.

Download: http://bit.ly/1YF37Ke

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: While the development of techniques that allow artificial agents to engage in dialogue with humans has received a lot of interest in the multiagent systems and natural language processing literature, most of the systems created to date have focused on specific domains and types of dialogue. This has led to agent designs that are useful for specific dialogue situations, but hard to adapt to different settings. The creation of more flexible agents that can deal with a broad range of communicative scenarios would greatly improve the interaction between agents and humans, and would eliminate the need to manually adapt the design of a conversational agent when dealing with a new task domain. In this paper, we present initial work toward creating agents that are able to generate task-oriented dialogues based on a description of a previously unknown domain. Our method is based on utilising automated planning methods, which are suitable for processing specifications both of the communication language to be used and of the domain in hand. We provide preliminary experimental results which suggest that our method has the potential to provide the flexibility required to produce a broad range of communication behaviours in different settings.

Keywords: Dialogue Planning, Conversational Agents, Human-Agent Dialogue.

Citation: Tânia Marques and Michael Rovatsos. Toward Domain-Independent Dialogue Planning.

Download: http://bit.ly/1VzKPg1

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: The majority of volunteers participating in citizen science projects perform only a few tasks each before leaving the system. We designed an intervention strategy to reduce disengagement in 16 different citizen science projects. Targeted users who had left the system received emails that directly addressed motivational factors that affect their engagement. Results show that participants receiving the emails were significantly more likely to return to productive activity when compared to a control group.

Keywords: Peer production, crowdsourcing, citizen science, intervention strategies.

Citation: Segal, A., Gal, Y.A.K., Simpson, R.J., Victoria Homsy, V., Hartswood, M., Page, K.R. and Jirotka, M., 2015, May. Improving productivity in citizen science through controlled intervention. In Proceedings of the 24th International Conference on World Wide Web Companion (pp. 331-337). International World Wide Web Conferences Steering Committee.

Download: http://bit.ly/1SiqBku

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Preventive maintenance is essential for the smooth operation of any equipment. Still, people occasionally do not maintain their equipment adequately. Alert systems attempt to remind people to perform maintenance. However, most of these systems do not provide alerts at the optimal timing, and nor do they take into account the time required for maintenance or compute the optimal timing for a specific user. In this paper we model the problem of maintenance performance, assuming maintenance is time consuming. We solve the optimal policy for the user, i.e., the optimal timing for a user to perform maintenance. This optimal strategy depends on the user’s value of time, and thus it may vary from user to user and may change over time. Based on the solved optimal strategy we present a personalized alert agent, which, depending on the user’s value of time, alerts the user when she should perform maintenance. In an experiment using a spaceship computer game, we show that receiving alerts from the personalized alert agent significantly improves user performance.

Citation: Avraham Shvartzon, Amos Azaria, Sarit Kraus, Claudia Goldman, Joachim Meyer and Omer Tsimhoni. Worth Fixing: Personalizing Maintenance Alerts for Optimal Performance.

Download: http://bit.ly/1WcKUVa

Abstract: With the advance of research in human computation, applications and software systems are increasingly being designed to include the human aspect of computation. We work with Social Compute Units (SCUs) that are computational constructs with people as their core resources. They are collaborative units, and have a cloud-like behavior in the sense that they may be elastically adapted at runtime. Systems that utilize the concept of SCUs bring challenges that are associated with the highly dynamic and unpredictable human-centric behavior. Thus, trust in the human based services that execute tasks is of paramount importance. While there is related work on social trust in the social networking and crowdsourcing areas, trust in highly coordinated team-based systems such as SCUs remains as a significant challenge. Thus, in this paper we provide a trust model that considers merging social trust with performance based trust of human based services into an integrated trust model. We illustrate the models’ application in concrete strategies, such as for elastic management of SCUs and incentives for SCU members.

Citation: Mirela Riveni, Hong-Linh Truong, Schahram Dustdar, “Trust-aware Elastic Social Compute Units”, The 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-15), Helsinki, Finland, 20-22 August 2015.

Download: http://bit.ly/1SnmHw4

Abstract: Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of socio-technical systems where both humans and machine peers complement each other and operate collectively to achieve their goals. These systems are characterized by the fundamental properties of hybridity and collectiveness, hiding from users the complexities associated with managing the collaboration and coordination of hybrid human/machine teams. In this paper we present the key programming elements of the SmartSociety HDA-CAS platform. We first describe the overall platform’s architecture and functionality and then present concrete programming model elements – Collective-based Tasks (CBTs) and Collectives, describe their properties and show how they meet the hybridity and collectiveness requirements. We also describe the associated Java language constructs, and show how concrete use-cases can be encoded with the introduced constructs.

Citation: O. Scekic, T. Schiavinotto, D. I. Diochnos, M. Rovatsos, H.-L. Truong, I. Carreras, S. Dustdar, Programming Model Elements for Hybrid Collaborative Adaptive Systems, 1st IEEE International Conference on Collaboration and Internet Computing (CIC’15), 27-30 October 2015, Hangzhou, China.

Citation: http://bit.ly/1p8SJOP

Abstract: Society is moving towards a socio-technical ecosystem in which physical and virtual dimensions of life are intertwined and where people interactions ever more take place with or are mediated by machines. Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of sociotechnical systems where humans and machines synergetically complement each other and operate collectively to achieve their goals. HDA-CAS introduce the fundamental properties of hybridity and collectiveness, hiding from the users the complexities associated with managing the collaboration and coordination of machine and human computing elements. In this paper we present an HDA-CAS system called SmartSociety, supporting computations with hybrid human/machine collectives. We describe the platform’s architecture and functionality, validate it on two real-world scenarios involving human and machine elements and present a performance evaluation.

Citation: O. Scekic, D. Miorandi, T. Schiavinotto, D. I. Diochnos, A. Hume, R. Chenu-Abente, H.-L. Truong, M. Rovatsos, I. Carreras, S. Dustdar, F. Giunchiglia, SmartSociety — A Platform for Collaborative People-Machine Computation, The 8th IEEE International Conference on Service Oriented Computing & Applications (SOCA’15), 19-21 October 2015, Rome, Italy.

Download: http://bit.ly/1Wz4eN5

Smart-Watch Life Saver:
Smart-Watch Interactive-Feedback System for Improving Bystander CPR

This potentially life saving technology, uses a smart-watch to unobtrusively guide its user in efficiently delivering CPR, and is SmartSociety’s first step in developing the Smart Nurse project.

The 19th International Symposium on Wearable Computers (ISWC 2015) was a conference dedicated to cutting-edge research in wearable technologies, and took place on 7-11 September in Osaka, Japan.

You can find the paper through our website, here, or in the conference proceedings, here.

Abstract: In this work a Smart-Watch application, that is able to monitor the frequency and depth of Cardiopulmonary Resuscitation (CPR) and provide interactive corrective feedback is described. We have evaluated the system with a total of 41 subjects who had undertaken a single episode of CPR training several years previously. This training was part of a First Aid course for lay people, commonly accessed in this population. The evaluation was conducted by measuring participant CPR competence using the “gold standard” of CPR training, namely frequency and compression depth. The evaluation demonstrated that the Smart Watch feedback system provided a significant improvement in the participant performance. For example, it doubled the number of people who could maintain both the parameters in the recommended range for at least 50% of the time.

doi: http://doi.acm.org/10.1145/2802083.2802086

Citation: Agnes Gruenerbl, Gerald Pirkl, Eloise Monger, Mary Gobbi, and Paul Lukowicz. 2015. Smart-watch life saver: smart-watch interactive-feedback system for improving bystander CPR. In Proceedings of the 2015 ACM International Symposium on Wearable Computers (ISWC ’15). ACM, New York, NY, USA, 19-26.

Citation: http://bit.ly/1NgCIlk

We are seeking a Research Associate to work on distributed systems and orchestration with a particular focus on how these can be realised and understood in the contexts of Web Architecture, specifically REST and Linked Data approaches, and of reasoning over, and allocation of, resources in dynamic and potentially incomplete environments.

The role is to assist in the research, design, development and evaluation of a system for compositionality and social orchestration and its deployment with the wider Smart Society platform. Building upon research into semantic workflow orchestration already undertaken in the ‘Smart Share’ application, the postholder will work towards the abstraction of these findings into generalised methods and mechanisms that will be applied in other Smart Society use cases.

This is a fixed-term post for 1 year.

The closing date for applications is 12.00 noon on 14 April 2015 with interviews being held on 24 April 2015.

For the complete description and contact information, please see the original posting here.

SmartSociety is taking part in ICT DAYS, an initiative by the University of Trento‘s DISI (Department of Information Engineering and Computer Science), dedicated to the broad subject of Information and Communication Technology.

ICT DAYS has had many years of success since its launch on 2009. It serves as a meeting point for companies and university students, organizes a hackathon and attracts prospective students through its Education Day. The initiative also features a series of communicative events, such as seminars, demos and contests.

SmartSociety’s panel on the 19th of March is titled:

Collaboration between humans and machines: Promises and Perils

Abstract: In today’s hybrid society we are unconsciously observed and continuously assisted by ICT systems. How to make sure that this takes place with the right coordination and ethical control? The panel will report insights from the project SMARTSOCIETY funded by the EC under FP7 Future Emerging Technologies programme.

For more information, please visit the event website.

For more information on this, as well as Imaginary’s recent and upcoming events, including their participation in CeBIT 2015 (Hannover, 16-20 March), please click here.

Abstract: On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The Open Provenance Model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations, and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present paper extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The paper concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs.

doi: http://eprints.soton.ac.uk/id/eprint/374183

Citation: Natalia Kwasnikowska, Luc Moreau, and Jan Van den Bussche. A formal account of the open provenance model. ACM Transactions on the Web, February 2015.

Download: http://bit.ly/2j900wX

Abstract: As users become confronted with a deluge of provenance data, dedicated techniques are required to make sense of this kind of information. We present Aggregation by Provenance Types, a provenance graph analysis that is capable of generating provenance graph summaries. It proceeds by converting provenance paths up to some length k to attributes, referred to as provenance types, and by grouping nodes that have the same provenance types. The summary also includes numeric values representing the frequency of nodes and edges in the original graph.Quantitative and qualitative evaluations and a complexity analysis show that this technique is tractable; with small values of k, it can produce useful summaries and can help detect outliers. We illustrate how the generated summaries can further be used for conformance checking and visualization.

doi: http://eprints.soton.ac.uk/id/eprint/364726

Citation: Luc Moreau. Aggregation by provenance types: A technique for summarising provenance graphs. In Graphs as Models 2015 (An ETAPS’15 workshop), London, UK, April 2015.

Download: http://bit.ly/2ioVQyZ

The deadline for paper submission to HAIDM 2015 has been extended to the February the 23rd.

Topics covered by this year’s HAIDM include amongst others:

• Trust between humans and agents
• Smart society applications including energy systems, ride-sharing, healthcare augmentation, and disaster response
• Coalition formation and optimisation models involving models of agents and humans
• Human-Robot Interaction
• Crowdsourcing
• Citizen science
• Enhanced models of human behaviour and theory of human behaviour
• Applications of human behaviour models,
• Behavioural game theory
• Techniques for learning human behaviour
• Quantitative and qualitative studies of human-agent interaction

Important Dates:
11th February: 23rd of February:  Submission deadline
10th March: Notifications
19th March: Deadline for Camera-Ready copies

For more details, including the submission procedure, visit the HAIDM 2015 site here.