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Equipping the ACT-R cognitive architecture with a temporal ratio model of memory and using it in a new intelligent adaptive interface

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Abstract

ACT-R, as a useful and well-known cognitive architecture, is a theory for simulating and understanding human cognition. However, the standard version of this architecture uses a deprecated forgetting model. So, we equipped it with a temporal ratio model of memory that has been named as SIMPLE (Scale-Independent Memory, Perception, and Learning). On the other hand, one of the usages of cognitive architectures is to model the user in an Intelligent Adaptive Interface (IAI) implementation. Thus, our motivation for this effort is to use this equipped ACT-R in an IAI to deliver the right information at the right time to users based on their cognitive needs. So, to test our proposed equipped ACT-R, we designed and implemented a new IAI to control a swarm of Unmanned Aerial Vehicles (UAVs). This IAI uses the equipped ACT-R for user cognitive modeling, to deliver the right information to the users based on their forgetting model. Thus, our contributions are: equipping the ACT-R cognitive architecture with the SIMPLE memory model and using this equipped version of ACT-R for user modeling in a new IAI to control a group of UAVs. Simulation results, which have been obtained using different subjective and objective measures, show that we significantly improved situation awareness of the users using the IAI empowered by our equipped ACT-R.

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Notes

  1. We used this test because it is more robust in case of unequal variances.

  2. Except α = 0.15, overall IAI operations, there was a significant difference. Also, the α = 0.15 case would in standard scientific practice not be seen as significant as the error probability is too high..

  3. It must be noted that the overall significance level is impacted by the lack of significance for the 3 UAV case. Also, the forgetting model has more impact in the 5 UAV case because the cognitive load is bigger in that case.

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Authors and Affiliations

  1. Iran University of Science and Technology (IUST), Tehran, Iran

    Mahdi Ilbeygi, Mohammad Reza Kangavari & S. Alireza Golmohammadi

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  1. Mahdi Ilbeygi
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  2. Mohammad Reza Kangavari
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  3. S. Alireza Golmohammadi
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Ilbeygi, M., Kangavari, M.R. & Golmohammadi, S.A. Equipping the ACT-R cognitive architecture with a temporal ratio model of memory and using it in a new intelligent adaptive interface. User Model User-Adap Inter 29, 943–976 (2019). https://doi.org/10.1007/s11257-019-09239-2

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