Human-Aware Planning: A Survey Related to Joint Human-Agent Activities

Ahrndt, Sebastian; Fähndrich, Johannes; Albayrak, Şahin

doi:10.1007/978-3-319-07476-4_12

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…XAIP originated from human-aware planning (HAP) [12,13], which focuses on behaviors that an autonomous system such as a robot should plan when performing tasks in collaboration with humans. Planning considering humans is necessary when a person enters a control loop, as opposed to the robot performing the task alone.…”

Section: Explainable Ai Planning (Xaip)mentioning

confidence: 99%

Explainable Autonomous Robots: A Survey and Perspective

Sakai¹,

Nagai

2021

Preprint

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Advanced communication protocols are critical to enable the coexistence of autonomous robots with humans. Thus, the development of explanatory capabilities is an urgent first step toward autonomous robots. This survey provides an overview of the various types of "explainability" discussed in machine learning research. Then, we discuss the definition of "explainability" in the context of autonomous robots (i.e., explainable autonomous robots) by exploring the question "what is an explanation?" We further conduct a research survey based on this definition and present some relevant topics for future research.

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Section: Explainable Ai Planning (Xaip)mentioning

confidence: 99%

Explainable Autonomous Robots: A Survey and Perspective

Sakai¹,

Nagai

2021

Preprint

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“…[17]). In particular, the use of human behavioural models providing insights into the human nature from the psychological point of view is desired [3,12]. These models form the theoretical basis for predicting human behaviour and can provide information about the personality, habits and capabilities of humans, e.g.…”

Section: Introductionmentioning

confidence: 99%

Predictability in human-agent cooperation

Ahrndt¹,

Breitung²,

Fähndrich³

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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Making artificial agents a constituent part of human activities leads to more affiliated teamwork scenarios and at the same time introduces several new challenges. One challenge is the team members' ability to be mutually predictable, which is required to effectively plan own actions, e.g., in the field of human-aware planning. This work approaches the question whether or not agents are able to learn the personality of a human during interaction. In particular, we developed an agent model able to learn human personality during repeatedly played rounds in the Colored Trails Game. Human personality is described using a psychological theory of personality types known as the Five-Factor Model. The results indicate that some characteristics of a personality can be learned more accurately/easily than others.

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