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: 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

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.