Fusion adaptive resonance theory

With a single pattern channel, the fusion ART architecture reduces to the original ART model. Using a selected vigilance value ρ, an ART model learns a set of recognition nodes in response to an incoming stream of input patterns in a continuous manner. Each recognition node in the ${\displaystyle F_{2}}$ field learns to encode a template pattern representing the key characteristics of a set of patterns. ART has been widely used in the context of unsupervised learning for discovering pattern groupings.

By synchronizing pattern coding across multiple pattern channels, fusion ART learns to encode associative mappings across distinct pattern spaces. A specific instance of fusion ART with two pattern channels is known as adaptive resonance associative map (ARAM), that learns multi-dimensional supervised mappings from one pattern space to another pattern space. An ARAM system consists of an input field ${\displaystyle F_{1}^{a}}$, an output field ${\displaystyle F_{1}^{b}}$, and a category field ${\displaystyle F_{2}}$. Given a set of feature vectors presented at ${\displaystyle F_{1}^{a}}$ with their corresponding class vectors presented at ${\displaystyle F_{1}^{b}}$, ARAM learns a predictive model (encoded by the recognition nodes in ${\displaystyle F_{2}}$) that associates combinations of key features to their respective classes.

Fuzzy ARAM, based on fuzzy ART operations, has been successfully applied to numerous machine learning tasks, including personal profiling,[12] document classification,[13] personalized content management,[14] and DNA gene expression analysis.[15] In many benchmark experiments, ARAM has demonstrated predictive performance superior to those of many state-of-the-art machine learning systems, including C4.5, Backpropagation Neural Network, K Nearest Neighbour, and Support Vector Machines.

During learning, fusion ART formulates recognition categories of input patterns across multiple channels. The knowledge that fusion ART discovers during learning, is compatible with symbolic rule-based representation. Specifically, the recognition categories learned by the ${\displaystyle F_{2}}$ category nodes are compatible with a class of IF-THEN rules that maps a set of input attributes (antecedents) in one pattern channel to a disjoint set of output attributes (consequents) in another channel. Due to this compatibility, at any point of the incremental learning process, instructions in the form of IF-THEN rules can be readily translated into the recognition categories of a fusion ART system. The rules are conjunctive in the sense that the attributes in the IF clause and in the THEN clause have an AND relationship. Augmenting a fusion ART network with domain knowledge through explicit instructions serves to improve learning efficiency and predictive accuracy.

The fusion ART rule insertion strategy is similar to that used in Cascade ARTMAP, a generalization of ARTMAP that performs domain knowledge insertion, refinement, and extraction.[16] For direct knowledge insertion, the IF and THEN clauses of each instruction (rule) is translated into a pair of vectors A and B respectively. The vector pairs derived are then used as training patterns for inserting into a fusion ART network. During rule insertion, the vigilance parameters are set to 1s to ensure that each distinct rule is encoded by one category node.

Reinforcement learning is a paradigm wherein an autonomous system learns to adjust its behaviour based on reinforcement signals received from the environment. An instance of fusion ART, known as FALCON (fusion architecture for learning and cognition), learns mappings simultaneously across multi-modal input patterns, involving states, actions, and rewards, in an online and incremental manner. Compared with other ART-based reinforcement learning systems, FALCON presents a truly integrated solution in the sense that there is no implementation of a separate reinforcement learning module or Q-value table. Using competitive coding as the underlying principle of computation, the network dynamics encompasses several learning paradigms, including unsupervised learning, supervised learning, as well as reinforcement learning.

FALCON employs a three-channel architecture, comprising a category field ${\displaystyle F_{2}}$ and three pattern fields, namely a sensory field ${\displaystyle F_{1}^{c1}}$ for representing current states, a motor field ${\displaystyle F_{1}^{c2}}$ for representing actions, and a feedback field ${\displaystyle F_{1}^{c3}}$ for representing reward values. A class of FALCON networks, known as TD-FALCON[11] ,incorporates Temporal Difference (TD) methods to estimate and learn value function Q(s,a), that indicates the goodness to take a certain action a in a given state s.

The general sense-act-learn algorithm for TD-FALCON is summarized. Given the current state s, the FALCON network is used to predict the value of performing each available action a in the action set A based on the corresponding state vector ${\displaystyle {\vec {s}}}$ and action vector ${\displaystyle {\vec {a}}}$. The value functions are then processed by an action selection strategy (also known as policy) to select an action. Upon receiving a feedback (if any) from the environment after performing the action, a TD formula is used to compute a new estimate of the Q-value for performing the chosen action in the current state. The new Q-value is then used as the teaching signal (represented as reward vector R) for FALCON to learn the association of the current state and the chosen action to the estimated value.