Computational metacognition

M Cox, Z Mohammad, S Kondrakunta… - arxiv preprint arxiv …, 2022‏ - arxiv.org
Computational metacognition represents a cognitive systems perspective on high-order
reasoning in integrated artificial systems that seeks to leverage ideas from human …

Stubborn: An environment for evaluating stubbornness between agents with aligned incentives

R Rachum, Y Nakar, R Mirsky - arxiv preprint arxiv:2304.12280, 2023‏ - arxiv.org
Recent research in multi-agent reinforcement learning (MARL) has shown success in
learning social behavior and cooperation. Social dilemmas between agents in mixed-sum …

When agents talk back: rebellious explanations

B Wright, M Roberts, DW Aha… - Proceedings of the 2019 …, 2019‏ - openreview.net
As the area of Explainable AI (XAI), and Explainable AI Planning (XAIP), matures, the ability
for agents to generate and curate explanations will likewise grow. We propose a new …

Rebel agents that adapt to goal expectation failures

Z Mohammad, MT Cox, M Molineaux - Proceedings of the Integrated …, 2020‏ - par.nsf.gov
Humans and autonomous agents often have differing knowledge about the world, the goals
they pursue, and the actions they perform. Given these differences, an autonomous agent …

A rebellion framework with learning for goal-driven autonomy

Z Mohammad - 2021‏ - corescholar.libraries.wright.edu
Modeling an autonomous agent that decides for itself what actions to take to achieve its
goals is a central objective of artificial intelligence. There are various approaches used to …

Anticipation in dynamic environments: Deciding what to monitor

ZA Dannenhauer - 2019‏ - corescholar.libraries.wright.edu
In dynamic environments, external changes may occur that may affect planning decisions
and goal choices. We claim that an intelligent agent should actively watch for what can go …

[فهرست منابع][C] When agents talk back: Rebellious explanations

A Person, B Person - 2019