Analysis of Evolutionary Behavior in Self-Learning Media Search Engines

TL;DR

This paper presents a self-learning multimedia search engine that uses reinforcement learning to adapt indexes dynamically, ensuring effective retrieval despite diverse media qualities and changing user interests.

Contribution

It introduces a novel reinforcement learning framework based on Markov Decision Processes for evolving search indexes in multimedia retrieval systems.

Findings

Indexes converge steadily over time

Reinforcement learning improves search performance

Evolutionary strategies balance exploration and exploitation

Abstract

The diversity of intrinsic qualities of multimedia entities tends to impede their effective retrieval. In a SelfLearning Search Engine architecture, the subtle nuances of human perceptions and deep knowledge are taught and captured through unsupervised reinforcement learning, where the degree of reinforcement may be suitably calibrated. Such architectural paradigm enables indexes to evolve naturally while accommodating the dynamic changes of user interests. It operates by continuously constructing indexes over time, while injecting progressive improvement in search performance. For search operations to be effective, convergence of index learning is of crucial importance to ensure efficiency and robustness. In this paper, we develop a Self-Learning Search Engine architecture based on reinforcement learning using a Markov Decision Process framework. The balance between exploration and exploitation is achieved through evolutionary exploration Strategies. The evolutionary index learning behavior is then studied and formulated using stochastic analysis. Experimental results are presented which corroborate the steady convergence of the index evolution mechanism. Index Term