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This work approaches the question whether or not agents are able to learn the personality of a human during interaction. We develop two agent-models to learn about the personality of humans during repeatedly played rounds in the Colored Trails Game. Human personality is described using a psychological theory of personality traits known as the Five-Factor Model. The results show that some characteristics of a personality can be learned more accurately than others. The work extends the state-of-the-art in that it does not follow a supervised learning approach requiring existing data sets.

Once artificial agents are able to support human beings and their activities we will see a shift from these days cooperative activities to more elaborated teamwork settings. There, agents are not only assistants but indeed become team members. Yet, such integration causes significant challenges. One of these challenges is to make the behaviour of all team members predictable in order to account for an effective collaboration. Interestingly, this challenge is not well-defined and different involved research areas refer to it using different terms. This paper explains what is meant by predictability eventually proposing a definition for the challenge. In doing so, we discuss predictability referring to joint human-agent activities, the involved learning problem, and the inherent planning part, which was named human-aware planning recently.

When the situation involves artificial and natural agents in the same environment that work together to achieve joint goals we talk about cooperative activities, human-agent teamwork, joint activities, or joint human-agent activities. Although, teamwork has become a widely accepted metaphor for multi-robot/multi-agent cooperation there are several challenges towards making agents “teammates” – some of them are present independently from the teams mixture, whereas others are particularly challenging for the development of human-agent teams. This work presents an overview about the challenges and brings together knowledge from the different involved research areas, further, we provide an attempt to define the term joint human-agent activity in order to motivate a discussion about the necessary elements.

Personality is one of the central elements determining the behaviour of humans. It influences other cognitive mechanisms such as emotions and moods and thus effects attention and actions. However, in the literature about cognitive agents, work that investigates the effects of personality is rare and somewhat disconnected. Bridging this gap represents one step towards conceptualising human behaviour in software agents, e.g. for resource-bounded agents in highly-dynamic environments or for virtual humans with realistic behaviour. The integration of personality in agents also requires its integration into reasoning processes used in agent-based systems. In this paper, we propose a formalisation that enables reasoning about the effectsand state of personality. This formalisation is integrated into the ‘\(\mathcal {L}\)ogic \(\mathcal {O}\)f \(\mathcal {R}\)ational \(\mathcal {A}\)gents’ (\(\mathcal {LORA}\)) and is the foundation for reasoning about the personality of other agents and the influence of personality on the action selection process.

Making artificial agents a constituent part of human activities leads to more affiliated teamwork scenarios and at the same time introduces several new challenges. One challenge is the team members' ability to be mutually predictable, which is required to effectively plan own actions, e.g., in the field of human-aware planning. This work approaches the question whether or not agents are able to learn the personality of a human during interaction. In particular, we developed an agent model able to learn human personality during repeatedly played rounds in the Colored Trails Game. Human personality is described using a psychological theory of personality types known as the Five-Factor Model. The results indicate that some characteristics of a personality can be learned more accurately/easily than others.

This work introduces a prototype that demonstrates the idea of using a psychological theory of personality types known as the Five-Factor Model (FFM) in planning for human-agent teamwork scenarios. FFM is integrated into the BDI model of agency leading to variations in the interpretation of inputs, the decision-making process and the generation of outputs. This is demonstrated in a multi-agent simulation. Furthermore, it is outlined how these variations can be used for the planning process in collaborative settings.

When it comes to planning for joint human-agent activities, one has to consider not only flexible plan execution and social constraints but also the dynamic nature of humans. This can be achieved by providing additional information about the characteristics of a human. As an example one need to take the physical and psychological condition of the elderly into consideration when developing collaborative applications like socially assistive robots. This work outlines Hplan, an extension to the agent-framework JIAC V, that takes this requirement into account. Hplan is strongly related to the conceptual model of dynamic planning components and integrates humans as avatars into a life cycle of planning, execution and learning.

This work presents an approach combining multiple electronic patient records (EPR) to a self-learning fall risk assessment tool. We utilized the agent-perspective to model the system, to address privacy issues and to evaluate different distributed information fusion and opinion aggregation techniques towards there applicability to the addressed domain. Each agent represents a single patient negotiating about unknown fall risk influences in order to adapt the fall-risk assessment tool to the population under care. In addition, we will outline the planned real-world case study.

Currently, there are many models available which can be used to describe a driver's behaviour for a traffic simulation. Despite the number of available formalisms it is our opinion that existing approaches neglect the interplay between the simulation topology and strategic decisions of simulated drivers. Existing models either disregard strategy updates or focus on short-term strategies only. In this paper we tackle this problem and propose a model which incorporates (long-term) strategic reactions of simulated drivers to influences of the surrounding infrastructure. In doing so, we formalise a certain type of human behaviour which is commonly known as strategic-level compensation.