Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

Langlois, Samuel; Akoroda, Oghenetekevwe; Carrillo, Estefany; Herrmann, Jeffrey W.; Azarm, Shapour; Xu, Huan; Otte, Michael W.

doi:10.1109/access.2020.3028751

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…Moreover, agents should consider the actions of multiple agents as they interact dynamically in the environment. In this case, system performance can be affected by metareasoning's effects (Langlois et al, 2020).…”

Section: Artificial Intelligence Approaches Aspects Of Metacognitionmentioning

confidence: 99%

Strengthening AI via ToM and MC dimensions

Bamicha,

Drigas

2024

Sci. Electronic Arch

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Theory of Mind (ToM) highlights the social-cognitive ability of the individual to communicate and interact effectively with the members of each social group. Essentially, it is the cornerstone of social knowledge that allows the recognition and understanding of the thoughts, intentions, and feelings of all involved, promoting social interaction and engagement. Metacognition (MC) is a higher mental ability of the biological mind and is characterized by the observation, control, evaluation, differentiation, and readjustment of the cognitive mechanism, aiming at its optimal performance and maintaining the homeostasis of mental, social, and emotional becoming of an organism. The rapid development of technology in recent decades has promoted the development of Artificial Intelligence (AI) intertwined with the need to integrate ToM and MC capabilities, enriching human communication. This paper investigates how the above-described human cognitive functions are involved in the conception and development of an artificial agent and their influence on human society. The conclusions suggest the importance of being able to read beliefs, emotions, and other factors, but also introspection by an intelligent system for social benefit, including the necessary ethical constraints.

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Section: Artificial Intelligence Approaches Aspects Of Metacognitionmentioning

confidence: 99%

Strengthening AI via ToM and MC dimensions

Bamicha,

Drigas

2024

Sci. Electronic Arch

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“…Algorithm selection has been used in numerous settings, including multi-agent search 35 and combinatorial and continuous optimization. [36][37][38][39] Langlois et al 40 reviewed previous work on metareasoning approaches for multi-agent systems.…”

Section: Metareasoningmentioning

confidence: 99%

Learning-based metareasoning for decision-making in multi-agent pursuit-evasion games

Namala,

Herrmann

2024

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI

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Reinforcement Learning (RL) has become a widely used approach for pursuit-evasion games. However, the behavior of such RL models is hard to analyze, often leading to a lack of trust. This paper describes a study in which we used machine learning (ML) approaches to develop metareasoning policies that control pursuers’ strategies. The proposed approach enables pursuer agents to capture a faster evader by choosing simple pursuit strategies collaboratively. The results show that some metareasoning policies perform better than any pursuer strategy combinations. Our approach provides an innovative way for the pursuer agents to reason about their opponents and adapt their strategy, which could have significant implications for the design of intelligent agents in real-world applications.

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“…Metareasoning as an anytime algorithm improves the agent's decision-making process based on the current situation [14], [15], [16], [17], [18], [19], [20]. As a result, the drone which is the agent can dynamically switch between cloud-based and edge computing implementation while considering the power consumption, latency constraints, and model accuracy metric.…”

Section: Introduction and Related Workmentioning

confidence: 99%

MLAE2: Metareasoning for Latency-Aware Energy-Efficient Autonomous Nano-Drones

Navardi

2023

2023 IEEE International Symposium on Circuits and Systems (ISCAS)

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Safety, low-cost, small size, and Artificial Intelligence (AI) capabilities of drones have led to the proliferation of autonomous tiny Unmanned Aerial Vehicles (UAVs) in many applications which are dangerous, unknown, or time-consuming for humans. Deep Neural Networks (DNNs) have enabled autonomous navigation while using captured data by drone sensors as input to the model. Due to the extreme complexity of DNNs, cloud-based approaches have been highly addressed in which a drone is connected to the cloud and sends the data to the cloud, and takes the result. On the other hand, emerging tiny machine learning models and edge computing brings significant improvement in energy efficiency and latency with respect to cloud-based approaches. However, there is a trade-off in these two implementations for model accuracy, latency, and energy efficiency. For instance, applying tiny machine learning models leads to lower latency but it sacrifices model accuracy in comparison to cloud-based computing. To address these challenges, we consider multiple models and introduce a new approach named MLAE2 which applies Metareasoning approach for Latency-Aware Energy-Efficient autonomous drones. Metareasoning monitors parameters such as latency and energy consumption for different algorithms and chooses the appropriate algorithm due to the environmental situation changes. To Evaluate our approach we extract the power consumption and latency for both cloudbased computing and edge computing while deploying multiple models on a tiny drone named Crazyflie. The experimental results show that MLAE2 successfully meets the latency constraint while maximizing model accuracy and improving energy efficiency.

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Metareasoning Structures, Problems, and Modes for Multiagent Systems: A Survey

Cited by 6 publications

References 27 publications

Strengthening AI via ToM and MC dimensions

Strengthening AI via ToM and MC dimensions

Learning-based metareasoning for decision-making in multi-agent pursuit-evasion games

MLAE2: Metareasoning for Latency-Aware Energy-Efficient Autonomous Nano-Drones

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