Abstract: Interactive interfaces are a common feature of many systems ranging from field robotics to video games. In most applications, these interfaces must be used by a heterogeneous set of users, with substantial variety in effectiveness with the same interface when configured differently. We address the issue of personalizing such an interface, adapting parameters to present the user with an environment that is optimal with respect to their individual traits – enabling that particular user to achieve their personal optimum. We introduce anew class of problem in interface personalization where the task of the adaptive interface is to choose the subset of actions of the full interface to present to the user. In formalizing this problem, we model the user as a Markov decision process (MDP), wherein the transition dynamics within a task depends on the type (e.g., skill or dexterity) of the user, where the type parametrizes the MDP. The action set of the MDP is divided into disjoint set of actions, with different action-sets optimal for different type (transition dynamics). The task of the adaptive interface is then to choose the right action-set.Given this formalization, we present experiments with simulated and human users in a video game domain to show that (a) action set selection is an interesting class of problems(b) adaptively choosing the right action set improves performance over sticking to a fixed action set and (c) immediately applicable approaches such as bandits can be improved upon.

Citation: M.M.H. Mahmud, B. Rosman, S. Ramamoorthy, P. Kohli. Adapting interaction environments to diverse users through online action set selection. In Proc. AAAI Workshop on Machine Learning for Interactive Systems (AAAI-MLIS), 2014.

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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.

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Abstract. Advances in human computation bring the feasibility of utilizing human capabilities as services. On the other hand, we have witnessed emerging collective adaptive systems which are formed from heterogeneous types of compute units to solve complex problems. The recently introduced Social Compute Units (SCUs) present one type of these systems, which have human-based services as their core fundamental compute units. While, there is related work on forming SCUs and optimizing their performance with adaptation techniques, most of it is focused on static structures of SCUs. To provide better runtime performance and exibility management for SCUs, we present an elasticity model for SCUs and mechanisms for their elastic management which allow for certain uctuations in size, structure, performance and quality. We model states of elastic SCUs, present APIs for managing SCUs as well as metrics for controlling their elasticity with which it is possible to tailor their performance parameters at runtime within the customer-set constraints. We illustrate our contribution with an example algorithm.

Keywords: social compute units, elasticity, adaptation, collective adaptive systems

doi: http://link.springer.com/chapter/10.1007%2F978-3-319-07881-6_25

Citation: Mirela Riveni, Hong-Linh Truong, Schahram Dustdar. On the Elasticity of Social Compute Units, Springer-Verlag, 26th International Conference on Advanced Information Systems Engineering (CAiSE 2014), 16-20 June 2014, Thessaloniki, Greece. Accepted.

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