Virtual agents have been shown to be more effective when incorporating social factors such as trust into task action selection. However, there has been less work on how virtual tutoring agents can incorporate social factors into pedagogical action ...

Although conversational dialogue systems are required for continuing long conversations with users to build relationships, they sometimes make sentences that are not related to the dialogue context, causing the dialogue to easily break down. We propose ...

We present in this paper a simulation-oriented theory of mind model for interpreting behaviors of power during a collaborative negotiation. This model relies on a model of negotiation that allows an agent to express behaviors of power through its ...

This paper demonstrates the integration and evaluation of different atmosphere models into Virtual Reality (VR) training for teacher education. We developed three behaviour models to simulate different levels of class discipline. We evaluated their ...

In order to collaborate with humans, robots are often provided with a Theory of Mind (ToM) architecture. Such architectures can be evaluated by humans perception of the robot's adaptations. However, humans sensitivities to these adaptations are not the ...

Change is at the core of conflict resolution. Conflicts provoke changes in other people's behaviours, beliefs or goals, and changes influence the state of conflict between the parties, making it a dynamic process over time. In this paper, we present a ...

We report an exploration into normative reasoning for robots in human societies using the concept of institutions.

Formation control is a canonical task in the multi-robot teamwork field, where a group of robots is required to maintain a specific geometric pattern, while moving from a start point to a destination. When one assumes imperfection of the sensors of the ...

Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state ...

Apprenticeship learning enables learning from human demonstrations performed on tasks. However, acquiring demonstrations in complex tasks where a human expert is not available can be a challenge. In this paper, we propose a new learning algorithm, ...

Reaching agreement through consensus is fundamental to the operation of distributed systems such as sensor networks, social networks or multi-robot networks. Consensus requires agents in the system to reach an agreement over a variable of interest only ...

Automating planning for large teams of heterogeneous robots is a growing challenge. The planning literature incorporates expressive features, but examples that scale to multiple robots in complex domains are limited and fail to generate feasible plans. ...

We assume that service robots will have spare time in between scheduled user requests, which they could use to perform additional unrequested services in order to learn a model of users' preferences and receive reward. However, a mobile service robot is ...

We propose a model for adaptation and implicit coordination in multi-robot systems based on the definition of artificial emotions, which play two main roles: modulators of individual robot behavior, and means of communication among different robots for ...

Human aware planning requires an agent to be aware of the mental model of the human in the loop during its decision process. This can involve generating plans that are explicable to the human as well as the ability to provide explanations when such ...

We introduce and empirically evaluate two techniques aimed at enhancing the performance of multi-robot prioritized path planning. The first technique is the deterministic procedure for re-scheduling (as opposed to well-known approach based on random ...

Allowing robots to understand their world in terms of affordances allows for generalization, learning, and complex planning, while also being intuitive for humans to understand. In recent work, affordances are often learned with hand-coded robot actions,...

As it achieves a goal on behalf of its human user, an autonomous agent's actions may have side effects that change features of its environment in ways that negatively surprise its user. An agent that can be trusted to operate safely should thus only ...

This note answers two central question in the intersection of decision-making and causal inference -- when human input is needed and, if so, how it should be incorporated into an AI system. We introduce the counterfactual agent who proactively considers ...