How Virtual Agents Can Learn to Synchronize: an Adaptive Joint Decision-Making Model of Psychotherapy

Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations

Decision-making model for adaptive impedance control of teleoperation systems

© 2008-2011 IEEE. This paper presents a haptic assistance strategy for teleoperation that makes a task and situation-specific compromise between improving tracking performance or human-machine interaction in partially structured environments via the scheduling of the parameters of an admittance controller. The proposed assistance strategy builds on decision-making models and combines one of them with impedance control techniques that are standard in bilateral teleoperation systems. Even though several decision-making models have been proposed in cognitive science, their application to assisted teleoperation and assisted robotics has hardly been explored yet. Experimental data supports the Drift-Diffusion model as a suitable scheduling strategy for haptic shared control, in which the assistance mechanism can be adapted via the parameters of reward functions. Guidelines to tune the decision making model are presented. The influence of the reward structure on the realized haptic assistances is evaluated in a user study and results are compared to the no assistance and human assistance case

Dynamic decision networks for decision-making in self-adaptive systems: a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision

Making Decision Adaptive to Price Uncertainty and Risk Preference: A New Decision-Making Model for Forest Management

While the forest grows, the price of timber fluctuates. Price uncertainty plays a key role in forestry due to the extended rotation length of growing trees. Like double sides of the same coin, risk preference and uncertainties should be considered together. This is because risk preference represents people’s attitude toward that uncertainty when making management decisions. Risk preference is especially an important issue for forest management because forests are exposed to substantial uncertainties during their long growing period. However, most existing relevant studies either simply overlook the risk preference issue or fail to consider it together with a practical forest management decision-making approach. In this dissertation study, a behavior-based forest management model was developed to measure forest managers’ risk preferences directly through their potential behaviors toward price changes. Besides, an adaptive harvest decision-making approach that incorporates varying levels of risk preference was established. Based on the models developed in this dissertation, numerical simulations were carried out to evaluate the impact of risk preferences in forest management outcomes. Results of simulations show that risk preference could indeed affect the performance of forest management. Besides, a properly selected risk preference level may bring extra risk premiums to forestry investment. In addition, sensitivity analyses found that there always exists a certain level of risk preference that will lead to the highest average return across different scenarios. Furthermore, a case study using the LSU Lee Memorial Forest as the sample site was carried out to demonstrate the adaptive harvest decision-making process using the method developed in prior chapters. The results of this case study not only confirmed the conclusions reached by numerical simulations, but also reiterated the importance of risk management strategy in forest management under uncertainties

Applications of decision theory to computer-based adaptive instructional systems

This paper considers applications of decision theory to the problem of instructional decision-making in computer-based adaptive instructional systems, using the Minnesota Adaptive Instructional System (MAIS) as an example. The first section indicates how the problem of selecting the appropriate amount of instruction in MAIS can be situated within the general framework of empirical Bayesian decision theory. The linear loss model and the classical test model are discussed in this context. The second section describes six characteristics essential in effective computerized adaptive instructional systems: (1) initial diagnosis and prescription; (2) sequential character of the instructional decision-making process; (3) appropriate amount of instruction for each student; (4) sequence of instruction; (5) instructional time control; and (6) advisement of learning need. It is shown that all but the sequence of instruction could be improved in MAIS with the extensions proposed. Several new lines of research arising from the application of psychometric theory to the decision component in MAIS are reviewed

An Adaptive System of Decision Making for Financial Markets

In the article an adaptive model of decision-making for financial markets based on the method of weighted indicators is considered. The model is built on signals from several standard mechanical trade systems (MTS) by generalizing and redistributing between them weight coefficients that change according to the effectiveness of the MTS. Calculations are per-formed by making use of price dynamic data from the international currency market FOREX.decision making; financial markets; currency market; adaptive system