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: This paper considers the problem of providing advice to an autonomous agent when neither the behavioural policy nor the goals of that agent are known to the advisor. We present an approach based on building a model of “commonsense” behaviour in the domain, from an aggregation of different users performing various tasks, modeled as MDPs, in the same domain. From this model, we estimate the normalcy of the trajectory given by a new agent in the domain, and provide behavioural advice based on an approximation of the trade-off in utility between potential benefits to the exploring agent and the costs incurred in giving this advice. This model is evaluated on a maze world domain by providing advice to different typesof agents, and we show that this leads to a considerable and unanimous improvement in the completion rate of their tasks.

Citation: B. Rosman, S. Ramamoorthy (2014). Giving advice to agents with hidden goals. In Proc. IEEE International Conference on Robotics and Automation (ICRA), 2014.

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

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: 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

EASSS 2014 will be held at the Technical University of Crete, Chania, Greece , from the 14th July to the 18th July 2014. Primarily focussed on single and multi-agent systems, this year’s school will feature additional courses covering topics related to social intelligence. It will be co-organised by the European Network for Social Intelligence and serves as their official Summer School.

Please note the deadline for early registration, which is the 31st May 2014. You can view the school’s complete schedule here.