When a Robot Reaches Out for Human Help

Andrés, Ignasi; Barros, Leliane Nunes de; Mauá, Denis Deratani; Simão, Thiago D.

doi:10.1007/978-3-030-03928-8_23

Cited by 3 publications

(2 citation statements)

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“…Another aspect is how to incorporate prior knowledge in uncertainty models. For instance, we might be able to query experts [9] or ask for demonstrations [116]. Such prior knowledge naturally gives rise to a distribution over models (similar to the Bayesian-Adaptive approaches discussed in Sect.…”

Section: Challenge 3: Incorporating Prior Knowledgementioning

confidence: 99%

Decision-making under uncertainty: beyond probabilities

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et al. 2023

Int J Softw Tools Technol Transfer

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This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty, but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion.

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Section: Challenge 3: Incorporating Prior Knowledgementioning

confidence: 99%

Decision-making under uncertainty: beyond probabilities

Badings

Simão

Suilen

et al. 2023

Int J Softw Tools Technol Transfer

Self Cite

View full text Add to dashboard Cite

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“…• Definition of optimization criteria for probabilistic planning with human help to minimize the use of human help and an empirical analysis of their performance (Andrés et al, 2018a).…”

Section: Contributionsmentioning

confidence: 99%

Human-help in automated planning under uncertainty

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ainda que políticas ótimas sob esses novos critérios podem ser computadas de maneira eficiente considerando que ações humanas possuem um custo alto ou penalizando o agente ao pedir ajuda humana. Soluções propostas em ambos cenários, planejamento contingente e planejamento probabilístico com ajuda humana, foram empiricamente avaliadas sobre um conjunto de problemas de planejamento com becos-sem-saida. Os resultados mostram que: (i) todas as políticas geradas (fortes (cíclicas) ou próprias) incluem ajuda humana somente quando necessária; e (ii) foram encontradas políticas para problemas de planejamento contingente com até 10 15000 estados de crença e para problemas de planejamento probabilístico com até 3 * 10 18 estados do mundo.

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