Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Storytelling Robot managing Persuasive and Ethical Stances via ACT-R: an Exploratory Study
137
0
0.0
(
0
)
Added by
Antonio Lieto
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We present a storytelling robot, controlled via the ACT-R cognitive architecture, able to adopt different persuasive techniques and ethical stances while conversing about some topics concerning COVID-19. The main contribution of the paper consists in the proposal of a needs-driven model that guides and evaluates, during the dialogue, the use (if any) of persuasive techniques available in the agent procedural memory. The portfolio of persuasive techniques tested in such a model ranges from the use of storytelling, to framing techniques and rhetorical-based arguments. To the best of our knowledge, this represents the first attempt of building a persuasive agent able to integrate a mix of explicitly grounded cognitive assumptions about dialogue management, storytelling and persuasive techniques as well as ethical attitudes. The paper presents the results of an exploratory evaluation of the system on 63 participants
rate research
Read More
Human collaborators can effectively communicate with their partners to finish a common task by inferring each others mental states (e.g., goals, beliefs, and desires). Such mind-aware communication minimizes the discrepancy among collaborators mental states, and is crucial to the success in human ad-hoc teaming. We believe that robots collaborating with human users should demonstrate similar pedagogic behavior. Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the users mental state. To evaluate our framework, we conduct a user study on a real-time human-robot cooking task. Experimental results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot. Code and video demos are available on our project website: https://xfgao.github.io/xCookingWeb/.
In computational cognitive science, the cognitive architecture ACT-R is very popular. It describes a model of cognition that is amenable to computer implementation, paving the way for computational psychology. Its underlying psychological theory has been investigated in many psychological experiments, but ACT-R lacks a formal definition of its underlying concepts from a mathematical-computational point of view. Although the canonical implementation of ACT-R is now modularized, this production rule system is still hard to adapt and extend in central components like the conflict resolution mechanism (which decides which of the applicable rules to apply next). In this work, we present a concise implementation of ACT-R based on Constraint Handling Rules which has been derived from a formalization in prior work. To show the adaptability of our approach, we implement several different conflict resolution mechanisms discussed in the ACT-R literature. This results in the first implementation of one such mechanism. For the other mechanisms, we empirically evaluate if our implementation matches the results of reference implementations of ACT-R.
Understanding the internals of Integrated Circuits (ICs), referred to as Hardware Reverse Engineering (HRE), is of interest to both legitimate and malicious parties. HRE is a complex process in which semi-automated steps are interwoven with human sense-making processes. Currently, little is known about the technical and cognitive processes which determine the success of HRE. This paper performs an initial investigation on how reverse engineers solve problems, how manual and automated analysis methods interact, and which cognitive factors play a role. We present the results of an exploratory behavioral study with eight participants that was conducted after they had completed a 14-week training. We explored the validity of our findings by comparing them with the behavior (strategies applied and solution time) of an HRE expert. The participants were observed while solving a realistic HRE task. We tested cognitive abilities of our participants and collected large sets of behavioral data from log files. By comparing the least and most efficient reverse engineers, we were able to observe successful strategies. Moreover, our analyses suggest a phase model for reverse engineering, consisting of three phases. Our descriptive results further indicate that the cognitive factor Working Memory (WM) might play a role in efficiently solving HRE problems. Our exploratory study builds the foundation for future research in this topic and outlines ideas for designing cognitively difficult countermeasures (cognitive obfuscation) against HRE.
Technology is an extremely potent tool that can be leveraged for human development and social good. Owing to the great importance of environment and human psychology in driving human behavior, and the ubiquity of technology in modern life, there is a need to leverage the insights and capabilities of both fields together for nudging people towards a behavior that is optimal in some sense (personal or social). In this regard, the field of persuasive technology, which proposes to infuse technology with appropriate design and incentives using insights from psychology, behavioral economics, and human-computer interaction holds a lot of promise. Whilst persuasive technology is already being developed and is at play in many commercial applications, it can have the great social impact in the field of Information and Communication Technology for Development (ICTD) which uses Information and Communication Technology (ICT) for human developmental ends such as education and health. In this paper we will explore what persuasive technology is and how it can be used for the ends of human development. To develop the ideas in a concrete setting, we present a case study outlining how persuasive technology can be used for human development in Pakistan, a developing South Asian country, that suffers from many of the problems that plague typical developing country.
The behavior of self driving cars may differ from people expectations, (e.g. an autopilot may unexpectedly relinquish control). This expectation mismatch can cause potential and existing users to distrust self driving technology and can increase the likelihood of accidents. We propose a simple but effective framework, AutoPreview, to enable consumers to preview a target autopilot potential actions in the real world driving context before deployment. For a given target autopilot, we design a delegate policy that replicates the target autopilot behavior with explainable action representations, which can then be queried online for comparison and to build an accurate mental model. To demonstrate its practicality, we present a prototype of AutoPreview integrated with the CARLA simulator along with two potential use cases of the framework. We conduct a pilot study to investigate whether or not AutoPreview provides deeper understanding about autopilot behavior when experiencing a new autopilot policy for the first time. Our results suggest that the AutoPreview method helps users understand autopilot behavior in terms of driving style comprehension, deployment preference, and exact action timing prediction.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
