scholarly journals Achieving Closed-Loop Control Simulation of Human-Artefact Interaction: A Comparative Review

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Wilhelm Frederik van der Vegte ◽  
Imre Horváth
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Low Cost ◽  
Simulation Models ◽  
Level Control ◽  
User Interactions ◽  
Loop Control ◽  
Low Level ◽  
Comparative Review ◽  
Human Decision

To include user interactions in simulations of product use, the most common approach is to couple human subjects to simulation models, using hardware interfaces to close the simulation-control loop. Testing with virtual human models could offer a low-cost addition to evaluation with human subjects. This paper explores the possibilities for coupling human and artefact models to achieve fully software-based interaction simulations. We have critically reviewed existing partial solutions to simulate or execute control (both human control and product-embedded control) and compared solutions from literature with a proof-of-concept we have recently developed. Our concept closes all loops, but it does not rely on validated algorithms to predict human decision making and low-level human motor control. For low-level control, validated solutions are available from other approaches. For human decision making, however, validated algorithms exist only to predict the timing but not the reasoning behind it. To identify decision-making schemes beyond what designers can conjecture, testing with human subjects remains indispensable.

scholarly journals Modeling and Reconstruction of State Variables for Low-Level Control of Soft Pneumatic Actuators

2021 ◽  
Vol 8 ◽  
Author(s):  
Serhat Ibrahim ◽  
Jan Christoph Krause ◽  
Alexander Olbrich ◽  
Annika Raatz
Keyword(s):  
Inertial Measurement Unit ◽  
Closed Loop ◽  
Measurement Unit ◽  
Closed Loop Control ◽  
State Variables ◽  
Level Control ◽  
Pneumatic Actuators ◽  
Loop Control ◽  
Inertial Measurement ◽  
Low Level

To further advance closed-loop control for soft robotics, suitable sensor and modeling strategies have to be investigated. Although there are many flexible and soft sensors available, the integration into the actuator and the use in a control loop is still challenging. Therefore, a state-space model for closed-loop low-level control of a fiber-reinforced actuator using pressure and orientation measurement is investigated. To do so, the integration of an inertial measurement unit and geometric modeling of actuator is presented. The piecewise constant curvature approach is used to describe the actuator’s shape and deformation variables. For low-level control, the chamber’s lengths are reconstructed from bending angles with a geometrical model and the identified material characteristics. For parameter identification and model validation, data from a camera tracking system is analyzed. Then, a closed-loop control of pressure and chambers’ length of the actuator is investigated. It will be shown, that the reconstruction model is suitable for estimating the state variables of the actuator. In addition, the use of the inertial measurement unit will demonstrate a cost-effective and compact sensor for soft pneumatic actuators.

scholarly journals Predicting human decision making in psychological tasks with recurrent neural networks

2021 ◽  
Author(s):  
Baihan Lin ◽  
Djallel Bouneffouf ◽  
Guillermo Cecchi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Prisoner's Dilemma ◽  
Iowa Gambling Task ◽  
Human Subjects ◽  
Prisoner’S Dilemma ◽  
Gambling Task ◽  
Iterated Prisoner's Dilemma ◽  
Human Decision ◽  
Iterated Prisoner’S Dilemma

Unlike traditional time series, the action sequences of human decision making usually involve many cognitive processes such as beliefs, desires, intentions and theory of mind, i.e. what others are thinking. This makes predicting human decision making challenging to be treated agnostically to the underlying psychological mechanisms. We propose to use a recurrent neural network architecture based on long short-term memory networks (LSTM) to predict the time series of the actions taken by the human subjects at each step of their decision making, the first application of such methods in this research domain. In this study, we collate the human data from 8 published literature of the Iterated Prisoner's Dilemma comprising 168,386 individual decisions and postprocess them into 8,257 behavioral trajectories of 9 actions each for both players. Similarly, we collate 617 trajectories of 95 actions from 10 different published studies of Iowa Gambling Task experiments with healthy human subjects. We train our prediction networks on the behavioral data from these published psychological experiments of human decision making, and demonstrate a clear advantage over the state-of-the-art methods in predicting human decision making trajectories in both single-agent scenarios such as the Iowa Gambling Task and multi-agent scenarios such as the Iterated Prisoner's Dilemma. In the prediction, we observe that the weights of the top performers tends to have a wider distribution, and a bigger bias in the LSTM networks, which suggests possible interpretations for the distribution of strategies adopted by each group.

SOFT COMPUTING: AN INTRODUCTION

2018 ◽  
Vol 8 (6) ◽  
pp. 63
Author(s):  
Matthew N. O. Sadiku ◽  
Yonghui Wang ◽  
Suxia Cui ◽  
Sarhan M. Musa
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Low Cost ◽  
Computational Techniques ◽  
Inaccurate Information ◽  
Major Benefit ◽  
Partial Truth ◽  
Human Decision

Soft computing (SC) is a newly emerging multidisciplinary field. It is a collection of computational techniques, such as expert systems, fuzzy logic, neural networks, and evolutionary algorithms, which provide information processing capabilities to solve complex practical problems. The major benefit of SC lies in its ability to tolerate imprecision, uncertainty, partial truth, and approximation in processing imprecise and inaccurate information and simulating human decision making at low cost. This paper provides a brief introduction on soft computing.

A Computational Role for Dopamine Delivery in Human Decision-Making

1998 ◽  
Vol 10 (5) ◽  
pp. 623-630 ◽  
Author(s):  
David M. Egelman ◽  
Christophe Person ◽  
P. Read Montague
Keyword(s):  
Decision Making ◽  
Human Subjects ◽  
Neural Mechanisms ◽  
Behavioral Strategies ◽  
Pharmacological Interventions ◽  
Decision Strategies ◽  
Human Decision ◽  
Future Events ◽  
New Interpretation ◽  
Mesencephalic Dopamine

Recent work suggests that fluctuations in dopamine delivery at target structures represent an evaluation of future events that can be used to direct learning and decision-making. To examine the behavioral consequences of this interpretation, we gave simple decision-making tasks to 66 human subjects and to a network based on a predictive model of mesencephalic dopamine systems. The human subjects displayed behavior similar to the network behavior in terms of choice allocation and the character of deliberation times. The agreement between human and model performances suggests a direct relationship between biases in human decision strategies and fluctuating dopamine delivery. We also show that the model offers a new interpretation of deficits that result when dopamine levels are increased or decreased through disease or pharmacological interventions. The bottom-up approach presented here also suggests that a variety of behavioral strategies may result from the expression of relatively simple neural mechanisms in different behavioral contexts.

scholarly journals Studying Collective Human Decision Making and Creativity with Evolutionary Computation

2015 ◽  
Vol 21 (3) ◽  
pp. 379-393 ◽  
Author(s):  
Hiroki Sayama ◽  
Shelley D. Dionne
Keyword(s):  
Decision Making ◽  
Evolutionary Computation ◽  
Interdisciplinary Research ◽  
Collaborative Design ◽  
Social Dynamics ◽  
Human Subjects ◽  
Computational Simulation ◽  
Idea Generation ◽  
Evolutionary Perspective ◽  
Human Decision

We report a summary of our interdisciplinary research project “Evolutionary Perspective on Collective Decision Making” that was conducted through close collaboration between computational, organizational, and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making, using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways—(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision-making processes, and (3) as a research tool for collecting high-resolution experimental data on actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.

scholarly journals Human-agent coordination in a group formation game

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuomas Takko ◽  
Kunal Bhattacharya ◽  
Daniel Monsivais ◽  
Kimmo Kaski
Keyword(s):  
Decision Making ◽  
Autonomous Agents ◽  
Human Subjects ◽  
Group Formation ◽  
Small World ◽  
Human Agent ◽  
Human Decision ◽  
Overall Performance ◽  
Behaviour Changes ◽  
Formation Game

AbstractCoordination and cooperation between humans and autonomous agents in cooperative games raise interesting questions on human decision making and behaviour changes. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to achieve connected clusters of the same colour by swapping places with neighbouring players using non-overlapping information. In the experiments the human players are incentivized by rewarding to prioritize their own cluster while the model of agents’ decision making is derived from our previous experiment of purely cooperative game between human players. The experiments were performed by grouping the players in three different setups to investigate the overall effect of having cooperative autonomous agents within teams. We observe that the human subjects adjust to autonomous agents by being less risk averse, while keeping the overall performance efficient by splitting the behaviour into selfish and cooperative actions performed during the rounds of the game. Moreover, results from two hybrid human-agent setups suggest that the group composition affects the evolution of clusters. Our findings indicate that in purely or lesser cooperative settings, providing more control to humans could help in maximizing the overall performance of hybrid systems.

Using Serious Games for Collecting and Modeling Human Procurement Decisions in a Supply Chain Context

2012 ◽  
pp. 135-156
Author(s):  
Souleiman Naciri ◽  
Min-Jung Yoo ◽  
Rémy Glardon
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Supply Chain ◽  
Operations Management ◽  
Simulation Models ◽  
Human Agent ◽  
Human Decision ◽  
Planning Processes ◽  
The Impact ◽  
A Company

Computer simulation is often used for studying specific issues in supply chains or for evaluating the impact of eligible design and calibration solutions on the performance of a company and its supply chain. In computer simulations, production facilities and planning processes are modeled in order to correctly characterize the supply chain behavior. However, very little attention has been given so far in these models to human decisions. Because human decisions are very complex and may vary across individuals or with time, they are largely neglected in traditional simulation models. This restricts the models’ reliability and utility. The first thing that must be done in order to include human decisions in simulation models is to capture how people actually make decisions. This chapter presents a serious game called DecisionTack, which was specifically developed to capture the human decision-making process in operations management (the procurement process). It captures both the information the human agent consults and the decisions he or she makes.

Using Serious Games for Collecting and Modeling Human Procurement Decisions in a Supply Chain Context

2012 ◽  
pp. 744-765
Author(s):  
Souleiman Naciri ◽  
Min-Jung Yoo ◽  
Rémy Glardon
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Supply Chain ◽  
Operations Management ◽  
Simulation Models ◽  
Human Agent ◽  
Human Decision ◽  
Planning Processes ◽  
The Impact ◽  
A Company

Computer simulation is often used for studying specific issues in supply chains or for evaluating the impact of eligible design and calibration solutions on the performance of a company and its supply chain. In computer simulations, production facilities and planning processes are modeled in order to correctly characterize the supply chain behavior. However, very little attention has been given so far in these models to human decisions. Because human decisions are very complex and may vary across individuals or with time, they are largely neglected in traditional simulation models. This restricts the models’ reliability and utility. The first thing that must be done in order to include human decisions in simulation models is to capture how people actually make decisions. This chapter presents a serious game called DecisionTack, which was specifically developed to capture the human decision-making process in operations management (the procurement process). It captures both the information the human agent consults and the decisions he or she makes.

Sign in / Sign up

Export Citation Format

Share Document