Information Fusion and Situation Awareness using ARTMAP and Partially Observable Markov Decision Processes

Brannon, Nathan Gregory; Conrad, Gregory N.; Draelos, Timothy J.; Seiffertt, John; Wunsch, Donald C.

doi:10.1109/ijcnn.2006.246950

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…The ART models described in the following subsections are used to perform reinforcement learning in which agents learn in real-time, incrementally and continuously by interacting with a complex and dynamic environment. ART-based reinforcement learning systems have found growing applications, for instance, in the computer games (da Silva & Goes, 2018;Wang et al, 2009;Wang & Tan, 2015) and situation awareness (Brannon et al, 2006(Brannon et al, , 2009 domains.…”

Section: Art Models For Reinforcement Learningmentioning

confidence: 99%

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A survey of adaptive resonance theory neural network models for engineering applications

2019

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This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers. 7 Code repositories 54 8 Conclusions 55 2 Compute activation function(s): Tj = fT (x, θj, α), ∀j ∈ C. 3 Perform WTA competition: J = arg max j∈Λ (Tj). 4 Compute match function(s):7 Update category J: θ new J = fL(x, θ old J , β). 8 else 9 Deactivate category J: Λ ← Λ − {J}. 10 if Λ = {∅} then 11 Go to step 3. 12 else 13 Set J = |C| + 1. 14 Create new category: C ← C ∪ {J}. 15 Initialize new category: θ new J = fN (x, λ). 16 Set output: y where the vigilance criterion is M J ≥ ρ.Learning. If the winning category satisfies the vigilance test, then resonance occurs, and the radius R J and centroid m J of the winning node are updated as follows:where ρ ∈ (0, 1] is the vigilance parameter.Learning. If the winning category J satisfies M J ≥ ρ, then resonance occurs, and it is updated as follows:J J needs to be updated after the presentation of each sample, then γ k (new) can be estimated incrementally. Particularly, when there is a resonant committed node J, if x k is a meta-information feature, thenotherwise,

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Section: Art Models For Reinforcement Learningmentioning

confidence: 99%

“…An important characteristic of this integration is the weight sharing between modalities. It uses a Markov Decision Process and Q-learning framework, and it has found application, for instance, in the field of situation awareness (Brannon et al, 2006(Brannon et al, , 2009.…”

Section: Unified Artmentioning

confidence: 99%

A survey of adaptive resonance theory neural network models for engineering applications

2019

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Coordinated machine learning and decision support for situation awareness

et al. 2009

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For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

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Validity index-based vigilance test in adaptive resonance theory neural networks

Silva

Wunsch

2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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Information Fusion and Situation Awareness using ARTMAP and Partially Observable Markov Decision Processes

Cited by 4 publications

References 6 publications

A survey of adaptive resonance theory neural network models for engineering applications

A survey of adaptive resonance theory neural network models for engineering applications

Coordinated machine learning and decision support for situation awareness

Validity index-based vigilance test in adaptive resonance theory neural networks

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