Ghazanfar F. Siddiqui

Coppélius' concoction: Similarity and complementarity among three affect-related agent models

In aiming for behavioral fidelity, artificial intelligence cannot and no longer ignores the formalization of human affect. Affect modeling plays a vital role in faithfully simulating human emotion and in emotionally-evocative technology that aims at being real. This paper offers a short expose about three models concerning the regulation and generation of affect: CoMERG, EMA and I-PEFiC^A^D^M, which each in their own right are successfully applied in the agent and robot domain. We argue that the three models partly overlap and where distinct, they complement one another. To enable their integration, we provide an analysis of the theoretical concepts, resulting in a more precise representation of affect simulation in virtual humans, which we verify with simulation tests.

A Virtual Therapist That Responds Empathically to Your Answers

Previous research indicates that self-help therapy is an effective method to prevent and treat unipolar depression. While web-based self-help therapy has many advantages, there are also disadvantages to self-help therapy, such as that it misses the possibility to regard the body language of the user, and the lack of personal feedback on the user responses. This study presents a virtual agent that guides the user through the Beck Depression Inventory (BDI) questionnaire, which is used to measure the severity of depression. The agent responds empathically to the answers given by the user, by changing its facial expression. This resembles face to face therapy more than existing web-based self-help therapies. A pilot experiment indicates that the virtual agent has added value for this application.

Comparing three computational models of affect

In aiming for behavioral fidelity, artificial intelligence cannot and no longer ignores the formalization of human affect. Affect modeling plays a vital role in faithfully simulating human emotion and in emotionally-evocative technology that aims at being real. This paper offers a short expose about three models concerning the generation and regulation of affect: CoMERG, EMA and I-PEFiCADM, which each in their own right are successfully applied in the agent and robot domain. We argue that the three models partly overlap and where distinct, they complement one another. We provide an analysis of the theoretical concepts, and provide a blueprint of an integration, which should result in a more precise representation of affect simulation in virtual humans.

Silicon Coppélia: Integrating Three Affect-Related Models for Establishing Richer Agent Interaction

Affect modeling plays a vital role in faithfully simulating human emotion and in emotionally evocative technology. Current affect models are still strong simplifications compared to human affective complexity. To establish richer agent interaction, we integrated three affect-related models: CoMERG, IPEFiC ADM and EMA. These models partly overlap, and where distinct, they complement one another. The integrated model called Silicon Coppélia was implemented and simulation experiments were performed to test the behavior of the model. These experiments show that the model can simulate richer agent behaviors than any of the models could have done alone.

Speed dating with an affective virtual agent: developing a testbed for emotion models

In earlier studies, user involvement with an embodied software agent and willingness to use that agent were partially determined by the aesthetics of the design and the moral fiber of the character. We used these empirical results to model agents that in their turn would build up affect for their users much the same way as humans do for agents. Through simulations, we tested these models for internal consistency and were successful in establishing the relationships among the factors as suggested by the earlier user studies. This paper reports on the first confrontation of our agent system with real users to check whether users recognize that our agents function in similar ways as humans do. Through a structured questionnaire, users informed us whether our agents evaluated the users aesthetics and moral stance while building up a level of involvement with the user and a degree of willingness to interact with the user again.

When the User Is Instrumental to Robot Goals: First Try - Agent Uses Agent

To create a robot with a mind of its own, we extended a formalized version of a model that explains affect-driven interaction with mechanisms for goal-directed behavior. We ran simulation experiments with intelligent software agents and found that agents preferred affect-driven decision options to rational decision options in situations where choices for low expected utility are irrational. This behavior counters current models in decision making, which generally have a hedonic bias and always select the option with the highest expected utility.

Comparative analysis of agent-based and population-based modelling in epidemics and economics

This paper addresses comparative evaluation of population-based simulation in comparison to agent-based simulation for different numbers of agents. Population-based simulation, such as for example in the classical approaches to predator-prey modelling and modelling of epidemics, has computational advantages over agent-based modelling with large numbers of agents. Therefore the latter approaches can be considered useful only when the results are expected to deviate from the results of population-based simulation, and are considered more realistic. However, there is sometimes also a silent assumption that for larger numbers of agents, agent-based simulations approximate population-based simulations, which would indicate that agent-based simulation just can be replaced by population-based simulation. The paper evaluates such assumptions by two detailed comparative case studies: one in epidemics, and one in economical context. The former case study addresses the spread of an infectious disease over a population. The latter case study addresses the interplay between individual greed as a psychological concept and global economical concepts. It is shown that under certain conditions agent-based and population-based simulations may show similar results, but not always.

An intelligent virtual agent to increase involvement in financial services

In order to enhance user involvement in financial services, this paper proposes to combine the idea of adaptive personalisation with intelligent virtual agents. To this end, a computational model for human decision making in financial context is incorporated within an intelligent virtual agent. To test whether the agent enhances user involvement, a web application has been developed, in which users have to make a number of investment decisions. This application has been evaluated in an experiment for a number of participants interacting with the system and afterwards providing their judgement by means of a questionnaire. The preliminary results indicate that the virtual agent can show appropriate emotional expressions related to states like happiness, greed and fear, and has high potential to enhance user involvement.

Supporting Financial Decision Making by an Intelligent Agent Estimating Greed and Risk

In the area of financial decision making it is more and more acknowledged that psychological states and characteristics play an important role, for example feeling insecure in relation to financial risks, and being greedy in relation to opportunities to obtain serious gains. This paper presents an agent model of human decision making behaviour in economic situations, incorporating a human’s greed state and personality characteristic concerning risk. The model provides a basis for the development of personalised intelligent agents that support a person in financial decisions. To evaluate the model a number of simulation experiments have been performed, which illustrate the model’s ability to show behaviour of different types of personalities.

An Affective Agent Playing Tic-Tac-Toe as Part of a Healing Environment

There is a growing belief that the environment plays an important role in the healing process of patients, supported by empirical findings. Previous research showed that psychological stress caused by loneliness can be reduced by artificial companions. As a pilot application for this purpose, this paper presents an affective agent playing tic-tac-toe with the user. Experimenting with a number of agents under different parameter settings shows the agent is able to show human-like emotional behavior, and can make decisions based on rationality as well as on affective influences. After discussing the application with clinical experts and making improvements where needed, the application can be tested in a clinical setting in future research.