Job interview simulation with a virtual agents aims at improving people's social skills and supporting professional inclusion. In such simulators, the virtual agent must be capable of representing and reasoning about the user's mental state based on social cues that inform the system about his her affects and social attitude. In this paper, we propose a formal model of Theory of Mind (ToM) for virtual agent in the context of human-agent interaction that focuses on the affective dimension. It relies on a hybrid ToM that combines the two major paradigms of the domain. Our framework is based on modal logic and inference rules about the mental states, emotions and social relations of both actors. Finally, we present preliminary results regarding the impact of such a model on natural interaction in the context of job interviews simulation.
To support such adaptability, we propose to rely on the BDI model. Several computational models of emotions have already been proposed (e.g. @cite_22 @cite_13 ) that show that BDI is a good basis to represent and to reason about the interlocutor's mental state. Our aim is thus to define a logical model of emotions and ToM in BDI.
@cite_13 @cite_22
111071380 2155250620
This article discusses a formal belief, desire, intention (BDI)-based agent model for theory of mind (ToM). The model uses BDI concepts to describe the reasoning process of an agent that reasons about the reasoning process of another agent, which is also based on BDI concepts. We discuss three different application areas and illustrate how the model can be applied to each of them. We explore a case study for each of the application areas and apply our model to it. For each case study, a number of simulation experiments are described, and their results are discussed. In this paper, we provide a logical formalization of the emotion triggering process and of its relationship with mental attitudes, as described in Ortony, Clore, and Collins’s theory. We argue that modal logics are particularly adapted to represent agents’ mental attitudes and to reason about them, and use a specific modal logic that we call Logic of Emotions in order to provide logical definitions of all but two of their 22 emotions. While these definitions may be subject to debate, we show that they allow to reason about emotions and to draw interesting conclusions from the theory. The challenges of robotic software testing extend beyond conventional software testing. Valid, realistic and interesting tests need to be generated for multiple programs and hardware running concurrently, deployed into dynamic environments with people. We investigate the use of BeliefDesire-Intention (BDI) agents as models for test generation, in the domain of human-robot interaction (HRI) in simulations. These models provide rational agency, causality, and a reasoning mechanism for planning, which emulate both intelligent and adaptive robots, as well as smart testing environments directed by humans. We introduce reinforcement learning (RL) to automate the exploration of the BDI models using a reward function based on coverage feedback. Our approach is evaluated using a collaborative manufacture example, where the robotic software under test is stimulated indirectly via a simulated human co-worker. We conclude that BDI agents provide intuitive models for test generation in the HRI domain. Our results demonstrate that RL can fully automate BDI model exploration, leading to very effective coverage-directed test generation. In this paper we focus on explaining to humans the behavior of autonomous agents, i.e., explainable agents. Explainable agents are useful for many reasons including scenario-based training (e.g. disaster training), tutor and pedagogical systems, agent development and debugging, gaming, and interactive storytelling. As the aim is to generate for humans plausible and insightful explanations, user evaluation of different explanations is essential. In this paper we test the hypothesis that different explanation types are needed to explain different types of actions. We present three different, generically applicable, algorithms that automatically generate different types of explanations for actions of BDI-based agents. Quantitative analysis of a user experiment (n=30), in which users rated the usefulness and naturalness of each explanation type for different agent actions, supports our hypothesis. In addition, we present feedback from the users about how they would explain the actions themselves. Finally, we hypothesize guidelines relevant for the development of explainable BDI agents.