A preliminary of this work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

This work was the winner of the “Best Student Paper Award” at the Seventh International Conference on Educational Data Mining (EDM 2014).

Abstract: The growing prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities, backgrounds and styles. There is thus a growing need to accommodate for individual differences in such e-learning systems. This paper presents a new algorithm for personalizing educational content to students that combines collaborative filtering algorithms with social choice theory. The algorithm constructs a “difficulty” ranking over questions for a target student by aggregating the ranking of similar students, as measured by different aspects of their performance on common past questions, such as grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for a target student, rather than ordering them according to predicted performance, which is prone to error. The algorithm was tested on two large real world data sets containing tens of thousands of students and a million records. Its performance was compared to a variety of personalization methods as well as a non-personalized method that relied on a domain expert. It was able to significantly outperform all of these approaches according to standard information retrieval metrics. Our approach can potentially be used to support teachers in tailoring problem sets and exams to individual students and students in informing them about areas they may need to strengthen..

Citation: EduRank: A Collaborative Filtering Approach to Personalization in E-learning Avi Segal, Ziv Katzir, Ya’akov Gal, Guy Shani and Bracha Shapira. EduRank: A Collaborative Filtering Approach to Personalization in E-learning. Seventh International Conference on Educational Data Mining (EDM 2014), London, England, July 2014.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Interaction between a user and a product like a home-appliance is sometimes a mutual relationship. A product not only provides service to a user, but also requires maintenance by the user to keep it working smoothly. If the user stops paying attention to maintenance, the product will no longer be used. To keep a sustainable relationship between a user and a product, the author proposes making a product an agent with anthropomorphic representation and interaction supported by the Anti-max Prisoner’s Dilemma game.

Keywords: Human-agent interaction, human interface, genetic programming.

Citation: Hirotaka Osawa. Sustainable Relationship with Product by Implementing Intentional Interaction.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: The multidisciplinary challenge of modelling agents have been driven by theory explaining social phenomena. Yet, these generic models lack of expressiveness. For that reason, data-driven approaches to the design of agents have been pursued, mainly for modelling non-verbal behaviour. In this paper we argue that real data is not only useful for that modality, but it can also assist agent’s design in different phases of the process at different levels of granularity. Furthermore, deep data, which inform us about user’s perception, emotions and motivations is valuable to build fluid interactions with virtual humans. We illustrate our stance with two case studies where we study interpersonal conflict. One study describes the design of agents to populate a serious game aimed at teaching conflict resolution skills to children and the other describes an experiment designed to extract deep data from a dyadic interaction prone to conflict emergence.

Keywords: Virtual Agents, Design, Interpersonal Conflict, Data-driven.

Citation: Joana Campos and Ana Paiva. Interactive Social Agents from Deep Data.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing support required to enable real-world types of interactions. In this paper we present NegoChat, the first negotiation agent that successfully addresses this limitation. NegoChat contains several significant research contributions. First, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead, the agents’ strategies must be modified to address partial agreements and issue-by-issue interactions. Second, we present NegoChat’s negotiation algorithm. This algorithm is based on bounded rationality, and specifically Aspiration Adaptation Theory (AAT). As per AAT, issues are addressed based on people’s typical urgency, or order of importance. If an agreement cannot be reached based on the value the human partner demands, the agent retreats, or downwardly lowers the value of previously agreed upon issues so that a “good enough” agreement can be reached on all issues. This incremental approach is fundamentally different from all other negotiation agents, including the state-of-the-art KBAgent. Finally, we present a rigorous evaluation of NegoChat, showing its effectiveness.

Keywords: Human-Agent Systems, Negotiation, Chat Agent.

Citation: Avi Rosenfeld. NegoChat: A Chat-Based Negotiation Agent.

Download: http://bit.ly/1XD4IQy

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Recently, many algorithms have been developed for autonomous agents to manage home energy use on behalf of their human owners. By so doing, it is expected that agents will be more efficient at, for example, choosing the best energy tariff to switch to when dynamically priced tariffs come about. However, to date, there has been no validation of such technologies in any field trial. In particular, it has not been shown whether users prefer fully autonomous agents as opposed to controlling their preferences manually. Hence, in this paper we describe a novel platform, called TariffAgent, to study notions of flexible autonomy in the context of tariff switching. TariffAgent uses real-world datasets and real-time electricity monitoring to instantiate a scenario where human participants may have to make, or delegate to their agent (in different ways), tariff switching decisions given uncertainties about their own consumption and tariff prices. We carried out a field trial with 10 participants and, from both quantitative and qualitative results, formulate novel design guidelines for systems that implement flexible autonomy.

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

Citation: Alper Alan, Enrico Costanza, Joel Fisher, Sarvapali Ramchurn, Tom Rodden and Nicholas Jennings. A Field Study of Human-Agent Interaction for Electricity Tariff Switching.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper proposes the Multi-Operation Patrol Scheduling System (MOPSS), a new system to generate patrols for transit system. MOPSS is based on five contributions. First, MOPSS is the first system to use three fundamentally different adversary models for the threats of fare evasion, terrorism and crime, generating three significantly different types of patrol schedule. Second, to handle uncertain interruptions in the execution of patrol schedules, MOPSS uses Markov decision processes (MDPs) in its scheduling. Third, MOPSS is the first system to account for joint activities between multiple resources, by employing the well known SMART security game model that tackles coordination between defender’s resources. Fourth, we are also the first to deploy a new Opportunistic Security Game model, where the adversary, a criminal, makes opportunistic decisions on when and where to commit crimes. Our fifth, and most important, contribution is the evaluation of MOPSS via real-world deployments, providing data from security games in the field.

Keywords: Security, Game-theory, Real-world deployment.

Citation: Francesco Delle Fave, Matthew Brown, Chao Zhang, Eric Shieh, Albert Jiang and Milind Tambe. Security Games in the Field: Deployments on a Transit System.

Download: http://bit.ly/1TcVx85

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper studies commitment strategies in three-player negotiation settings comprising human players and computer agents. We defined a new game called the Contract Game which is analogous to real-world market settings in which participants need to reach agreement over contracts in order to succeed. The game comprises three players, two service providers and one customer. The service providers compete to make repeated contract offers to the customer consisting of resource exchanges in the game. We formally analyzed the game and defined sub-game perfect equilibrium strategies for the customer and service providers that involve commitments. We conducted extensive empirical studies of these strategies in three different countries, the U.S., Israel and China. We ran several configurations in which two human participants played a single agent using the equilibrium strategies in various role configurations in the game (both customer and service providers). Our results showed that the computer agent using equilibrium strategies for the customer role was able to outperform people playing the same role in all three countries. In contrast, the computer agent playing the role of the service provider was not able to outperform people. Analysis reveals this difference in performance is due to the contracts proposed in equilibrium being significantly beneficial to the customer players, as well as irrational behavior taken by human customer players in the game.

Citation: Galit Haim, Kobi Gal, Bo An and Sarit Kraus. Equilibrium Strategies for Human-Computer Negotiation in 3-player market settings.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper deals with platforms that provide agents easier access to the type of opportunities in which they are interested (e.g., eCommerce platforms, used cars bulletins and dating web-sites). We show that under various common service schemes, a platform can benefit from not necessarily listing all the opportunities with which it is familiar, even if there is no marginal cost for listing any additional opportunity. The main implication of this result is that platforms should extract their expected-profit-maximizing service terms not based solely on the fees charged from users, but they should also use the subset that will be listed as the decision variable in the optimization problem. The analysis applies to four well-known service schemes that a platform may use to price its services. We show that neither of these schemes generally dominates the others or is dominated by any of the others. For the common case of homogeneous preferences, however, several dominance relationships can be proved, enabling the platform to identify the schemes that should be used as a default. Furthermore, the analysis provides a game-theoretic search-based explanation for a possible preference of buyers to pay for the service rather than receive it for free (e.g., when the service is sponsored by ads), a phenomena that has been justified in prior literature typically with the argument of willingness to pay a premium for an ad-free experience or more reliable platforms. The paper shows that this preference can hold both for the users and the platform in a given setting, even if both sides are fully strategic.

Keywords: Platforms and Services, Economics of Information, Two-Sided Markets, Price of Free, Service Schemes.

Citation: Chen Hajaj and David Sarne. Strategic Information Platforms – Selective Disclosure and The Price of “Free”.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Peer Designed Agent (PDA), computer agents developed by non-experts, is an emerging technology, widely advocated in recent literature for the purpose of replacing people in simulations and investigating human behavior. Its main premise is that the strategy programmed into these agents reliably reflect, to some extent, the behavior used by the programmer in real life. In this paper we show that PDA development has an important side effect that has not been addressed to date — the process, that merely attempts to capture one’s strategy, is also likely to affect the developer’s strategy. The phenomenon is demonstrated experimentally via the penetration detection game, using different setting variations. This result has many implications concerning the appropriate design of PDA-based simulations, and the validness of using PDAs for studying individual decision making.

Keywords: PDAs, decision making, simulation design.

Citation: Avshalom Elmalech, David Sarne and Noa Agmon. Peer Designed Agents: Just reflect or also affect?.

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

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Choice selection processes are a family of bilateral games of incomplete information in which a computer agent generates advice for a human user while considering the effect of the advice on the user’s behavior in future interactions. The human and the agent may share certain goals, but are essentially self-interested. This paper extends selection processes to settings in which the actions available to the human are ordered and thus the user may be influenced by the advice even though he doesn’t necessarily follow it exactly. In this work we also consider the case in which the user obtains some observation on the sate of the world. We propose several approaches to model human decision making in such settings. We incorporate these models into two optimization techniques for the agent advice provision strategy. In the first one the agent used a social utility approach which considered the benefits and costs for both agent and person when making suggestions. In the second approach we simplified the human model in order to allow modeling and solving the agent strategy as an MDP. In an empirical evaluation involving human users on AMT, we showed that the social utility approach significantly outperformed the MDP approach.

Keywords: Human modeling, advice provision, persuasion.

Citation: Amos Azaria, Sarit Kraus, Claudia Goldman and Kobi Gal. Advice Provision for Choice Selection Processes with Ranked Options.

Download: http://bit.ly/1U002VV