Analysis of empirical results on argumentation-based dialogue to support shared decision making in a human-robot team

Azhar, Mohd; Sklar, Elizabeth

doi:10.1109/roman.2016.7745220

Cited by 5 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third decision point could be reached multiple times per game, as participants sought to identify multiple treasures during each game. Detailed analysis of the decision points and dialogues that were used in this study is beyond the scope of this article, but has been presented elsewhere (Azhar and Sklar, 2016).…”

Section: Methodsmentioning

confidence: 99%

A study measuring the impact of shared decision making in a human-robot team

Azhar

Sklar

2017

The International Journal of Robotics Research

View full text Add to dashboard Cite

This paper presents the results of a user study in which the impact of sharing decision making in a human-robot team was measured. In the experiments outlined here, a human and robot play a game together in which the robot searches an arena for items, with input from the human, and the human-robot team earns points for finding and correctly identifying the items. The user study reported here involved 60 human subjects. Each subject interacted with two different robots. With one robot, the human acted as a supervisor: the human issued commands and the robot obeyed. With the other robot, the human acted as a collaborator: the human and robot shared decisions and were required to reach agreement about the robot’s actions in the arena before any actions were taken, facilitated using computational argumentation. Objective performance metrics were collected and analyzed for both types of human-robot team, as well subjective feedback from human subjects regarding attitudes toward working with a robot. The objective results showed significant improvement in performance metrics with the human-as-collaborator pairs versus the human-as-supervisor pairs. Subjective results demonstrated significant differences across many subjective measures and indicated a distinct preference for the human-as-collaborator mode. The primary contribution of this work lies in the demonstration and evaluation of a computational argumentation approach to human-robot interaction, particularly in proving the efficacy of this approach over a less autonomous mode of interaction.

show abstract

Section: Methodsmentioning

confidence: 99%

A study measuring the impact of shared decision making in a human-robot team

Azhar

Sklar

2017

The International Journal of Robotics Research

View full text Add to dashboard Cite

show abstract

“…In this case, analogously to the approach in [22] and differently from the approach in [16], time continues to elapse, via τ -actions, also for the timeout process. Axiom Chk (4) shows that, if the timeout is exceeded, i.e., the counter t has reached the value of 0, then the process check t (Arg ′ , R ′ ) → A fails.…”

Section: Syntax and Semanticsmentioning

confidence: 99%

“…Moreover, to simulate human behaviour, intelligent agents operating within distributed systems should communicate with each other through forms of interaction, like debate, that mirror those used by human beings. Debate constitutes, for example, the basis of multi-agent applications for decision making - [4], planning - [36], opinion polling - [42], and negotiation - [2]. We will use the terms dialogue and debate interchangeably, as both can be modelled in the same way and differ only in purpose: the former is typically intended in a collaborative sense, while the latter is usually oppositional.…”

Section: Introductionmentioning

confidence: 99%

Timed Concurrent Language for Argumentation: An Interleaving Approach

Bistarelli

Meo

Taticchi

2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Time is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents' behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the specification of temporal intervals in which particular actions occur. Such a language exploits a timed version of Abstract Argumentation Frameworks to realise a shared memory used by the agents to communicate and reason on the acceptability of their beliefs with respect to a given time interval. An interleaving model on a single processor is used for basic computation steps, with maximum parallelism for time elapsing. Following this approach, only one of the enabled agents is executed at each moment. To demonstrate the capabilities of language, we also show how it can be used to model interactions such as debates and dialogue games taking place between intelligent agents. Lastly, we present an implementation of the language that can be accessed via a web interface. Under consideration in Theory and Practice of Logic Programming (TPLP).

show abstract

“…Argumentation for shared decision-making between a robotic platform and a human is the topic in Sklar and Azhar (2015, 2018), Azhar and Sklar (2016, 2017). To the best of our knowledge, these were the first studies on argumentation-based dialogue games between a human and a robotic platform.…”

Section: Argumentation and Explainable Systemsmentioning

confidence: 99%

“…The authors developed an LBAF along with a GUI that helps humans in cooperation with a robotic platform to reach a shared decision at each step of an activity, more specifically in a Treasure Hunt Game (Sklar & Azhar, 2015). Next, in Azhar and Sklar (2016, 2017), Sklar and Azhar (2018), the authors extent the Treasure Hunt Game with different methodologies for implementing multiple types of argumentation-based dialogues. The framework can explain which dialogues are appropriate given the beliefs of the participants and how multiple dialogues can occur simultaneously while containing consistency in the general dialogue.…”

Section: Argumentation and Explainable Systemsmentioning

confidence: 99%

Argumentation and explainable artificial intelligence: a survey

Vassiliades

Bassiliades

Patkos

2021

The Knowledge Engineering Review

View full text Add to dashboard Cite

Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.

show abstract

Analysis of empirical results on argumentation-based dialogue to support shared decision making in a human-robot team

Cited by 5 publications

References 23 publications

A study measuring the impact of shared decision making in a human-robot team

A study measuring the impact of shared decision making in a human-robot team

Timed Concurrent Language for Argumentation: An Interleaving Approach

Argumentation and explainable artificial intelligence: a survey

Contact Info

Product

Resources

About