Trust and Decision Making in Turing's Imitation Game

2019 ◽  
pp. 195-210
Author(s):  
Huma Shah ◽  
Kevin Warwick
Keyword(s):  
Decision Making ◽  
Turing Test ◽  
Conversational Analysis ◽  
Human Machine Interaction ◽  
Experimental Conditions ◽  
Present Evidence ◽  
Post Test ◽  
Simultaneous Comparison ◽  
Machine Interaction ◽  
Turing’S Imitation Game

Trust is an expected certainty in order to transact confidently. However, how accurate is our decision-making in human-machine interaction? In this chapter, the present evidence from experimental conditions in which human interrogators used their judgement of what constitutes a satisfactory response trusting a hidden interlocutor was human when it was actually a machine. A simultaneous comparison Turing test is presented with conversation between a human judge and two hidden entities during Turing100 at Bletchley Park, UK. Results of post-test conversational analysis by the audience at Turing Education Day show more than 30% made the same identification errors as the Turing test judge. Trust is found to be misplaced in subjective certainty that could lead to susceptibility to deception in cyberspace.

Trust and Decision Making in Turing's Imitation Game

2018 ◽  
pp. 251-264
Author(s):  
Huma Shah ◽  
Kevin Warwick
Keyword(s):  
Decision Making ◽  
Turing Test ◽  
Conversational Analysis ◽  
Human Machine Interaction ◽  
Experimental Conditions ◽  
Present Evidence ◽  
Post Test ◽  
Simultaneous Comparison ◽  
Machine Interaction ◽  
Turing’S Imitation Game

Trust is an expected certainty in order to transact confidently. However, how accurate is our decision-making in human-machine interaction? In this chapter we present evidence from experimental conditions in which human interrogators used their judgement of what constitutes a satisfactory response trusting a hidden interlocutor was human when it was actually a machine. A simultaneous comparison Turing test is presented with conversation between a human judge and two hidden entities during Turing100 at Bletchley Park, UK. Results of post-test conversational analysis by the audience at Turing Education Day show more than 30% made the same identification errors as the Turing test judge. Trust is found to be misplaced in subjective certainty that could lead to susceptibility to deception in cyberspace.

The Finite Intersection Test: A New Multivariate Statistical Technique Applicable to the Evaluation of Complex Systems

1980 ◽  
Vol 24 (1) ◽  
pp. 453-457 ◽  
Author(s):  
John M. Reising ◽  
P.R. Krishnaiah
Keyword(s):  
Statistical Technique ◽  
System Evaluation ◽  
Finite Intersection ◽  
Multivariate Statistical ◽  
Human Machine Interaction ◽  
Single Measure ◽  
Simultaneous Comparison ◽  
Machine Systems ◽  
Machine Interaction ◽  
Intersection Test

In complex human-machine systems, multidimensional behavior is required of the operator. Consequently, there is no commonly-accepted, single measure of operator performance which can be utilized to determine the efficiency of the human-machine interaction. Because the behavior is multidimensional, multivariate statistics must be used to analyze the multiple measures gathered during system evaluation. While multivariate analogues to analysis of variance (ANOVA) exist, there are also a number of candidate multivariate analogues to the post-ANOVA simultaneous comparison tests. This paper describes a newly developed multivariate, simultaneous comparison test–Finite Intersection Test (FIT)–and provides an example of FIT's application to the analysis of multivariate data.

scholarly journals Artificial Intelligence Techniques for Conflict Resolution

2021 ◽  
Author(s):  
Reyhan Aydoğan ◽  
Tim Baarslag ◽  
Enrico Gerding
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Conflict Resolution ◽  
Computational Models ◽  
Group Decision ◽  
Preference Aggregation ◽  
Automated Negotiation ◽  
Special Issue ◽  
Human Machine Interaction ◽  
Machine Interaction

AbstractConflict resolution is essential to obtain cooperation in many scenarios such as politics and business, as well as our day to day life. The importance of conflict resolution has driven research in many fields like anthropology, social science, psychology, mathematics, biology and, more recently, in artificial intelligence. Computer science and artificial intelligence have, in turn, been inspired by theories and techniques from these disciplines, which has led to a variety of computational models and approaches, such as automated negotiation, group decision making, argumentation, preference aggregation, and human-machine interaction. To bring together the different research strands and disciplines in conflict resolution, the Workshop on Conflict Resolution in Decision Making (COREDEMA) was organized. This special issue benefited from the workshop series, and consists of significantly extended and revised selected papers from the ECAI 2016 COREDEMA workshop, as well as completely new contributions.

scholarly journals The Ethical Significance of Human Likeness in Robotics and AI

2019 ◽  
Vol 10 (2) ◽  
pp. 52-67 ◽  
Author(s):  
Peter Remmers
Keyword(s):  
Decision Making ◽  
Ethical Issues ◽  
Human Action ◽  
Artificial Agents ◽  
Human Machine Interaction ◽  
Human Likeness ◽  
Machine Interaction ◽  
Ethical Significance ◽  
And Robotics ◽  
Turing's Test

A defining goal of research in AI and robotics is to build technical artefacts as substitutes, assistants or enhancements of human action and decision-making. But both in reflection on these technologies and in interaction with the respective technical artefacts, we sometimes encounter certain kinds of human likenesses. To clarify their significance, three aspects are highlighted. First, I will broadly investigate some relations between humans and artificial agents by recalling certain points from the debates on Strong AI, on Turing’s Test, on the concept of autonomy and on anthropomorphism in human-machine interaction. Second, I will argue for the claim that there are no serious ethical issues involved in the theoretical aspects of technological human likeness. Third, I will suggest that although human likeness may not be ethically significant on the philosophical and conceptual levels, strategies to use anthropomorphism in the technological design of human-machine collaborations are ethically significant, because artificial agents are specifically designed to be treated in ways we usually treat humans.

scholarly journals Perceptual Maps to Aggregate Information from Decision Makers

2021 ◽  
Author(s):  
Olga Porro ◽  
Francesc Pardo-Bosch ◽  
Mónica Sánchez ◽  
Núria Agell
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Granular Structure ◽  
Decision Makers ◽  
Location Decisions ◽  
Illustrative Case ◽  
Human Machine Interaction ◽  
Aggregate Information ◽  
Perceptual Maps ◽  
Machine Interaction

Understanding different perceptions of human being when using linguistic terms is a crucial issue in human-machine interaction. In this paper, we propose the concept of perceptual maps to model human opinions in a group decision-making context. The proposed approach considers a multi-granular structure using unbalanced hesitant linguistic term sets. An illustrative case is presented in the location decisions made by multinationals enterprises of the energy sector within the European smart city context.

scholarly journals An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xiaolie Wu ◽  
Kezhong Liu ◽  
Jinfen Zhang ◽  
Zhitao Yuan ◽  
Jiongjiong Liu ◽  
...  
Keyword(s):  
Decision Making ◽  
Collision Avoidance ◽  
Autonomous System ◽  
Human Machine Interaction ◽  
Water Transportation ◽  
Risk Appetite ◽  
International Regulations ◽  
Decision Making System ◽  
Ship Collision ◽  
Machine Interaction

Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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