Balancing Explicability and Explanation in Human-Aware Planning
By Sarath Sreedharan, Tathagata Chakraborti, Subbarao Kambhampati. 2017
45 citations recorded by the source corpus.
Human aware planning requires an agent to be aware of the intentions, capabilities and mental model of the human in the loop during its decision process. This can involve generating plans that are explicable to a human observer as well as the ability to provide explanations when such plans cannot be generated. This has led to the notion "multi-model planning" which aim to incorporate effects of human expectation in the deliberative process of a planner - either in the form of explicable task planning or explanations produced thereof. In this paper, we bring these two concepts together and show how a planner can account for both these needs and achieve a trade-off during the plan generation process itself by means of a model-space search method MEGA. This in effect provides a comprehensive perspective of what it means for a decision making agent to be "human-aware" by bringing together existing principles of planning under the umbrella of a single plan generation process. We situate our discussion specifically keeping in mind the recent work on explicable planning and explanation generation, and illustrate these concepts in modified versions of two well known planning domains, as well as a demonstration on a robot involved in a typical search and reconnaissance task with an external supervisor.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
- Subbarao Kambhampati
- Sarath Sreedharan
- Tathagata Chakraborti
- Sergio Jiménez
- Eva Onaindia
- Diego Aineto
- Miquel Ramírez
In Computer Vision and Pattern Recognition
Citations in the corpus
- Handling Model Uncertainty and Multiplicity in Explanations via Model Reconciliation (2018, methodology): “A solution to an MRP (Chakraborti et al. 2017) is the set of model changes E or an explanation , such that the given plan π is optimal in both the robot model M R and the updated human model M RH .”
- Explainable Agents and Robots: Results from a Systematic Literature Review (2019, background): “2In contrast to explainable agent where explicit explanation is required, [94] defines explicabile system as one that avoid the need to provide explanations by generating plans that match the human’s expected plan.”