Abstract
Achieving decision-making that resembles humans is still a challenge for artificial intelligence (AI). Although researchers have successfully used techniques like deep reinforcement learning (DRL) and imitation learning (IL) to develop intelligent behavior in agents, however, such machine-learning-based methods may not resemble human choices. This study addresses this limitation by evaluating how a cognitive model based upon instance-based learning (IBL) theory matches human behavior on a simulation-based search-and-retrieval task. First, the simulation environment was developed using the Unity3D game engine. Next, four human players were recruited to play the simulation to generate human data. This data was then used to initialize the IBL models. In this research, we attempted to improve the quality of human data by sampling portions from the behavior data of multiple humans while maintaining the data size equivalent to the average size of each human’s data. Results revealed that the models driven by the multi-human data doubled in the accuracy of matching the human choices. We also present a novel depiction of how the IBL model’s decision-making improves with the variation in the number of human sources. Techniques where learning from human demonstrations is involved (e.g., IL) may benefit from these results by using multi-human data due to reduced noise and biases.
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Gupta, A., Uttrani, S., Paul, G., Kanekar, B., Dutt, V. (2023). Multi-human Intelligence in Instance-Based Learning. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_46
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DOI: https://doi.org/10.1007/978-981-99-1642-9_46
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