Autonomous Target Selection via Neural Networks
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Solution Overview
Problem
Traditional target selection systems for agents like spacecraft, landcraft, and watercraft rely heavily on human intervention and heuristic methods, which are time-consuming and inflexible, failing to leverage machine learning capabilities for optimal performance.
Innovation Solution
A computing system utilizing neural networks, specifically trained value and policy networks, to autonomously select targets by generating expected reward values and determining optimal actions based on input parameters, reducing human intervention and improving flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional heuristic methods or human operators are used for target selection, then the system can operate with simple architecture, but the process becomes time-consuming and requires extensive human intervention
Solution Approach 1:
The patent replaces traditional mechanical decision-making processes (human operators and heuristic algorithms) with a neural network-based cognitive system. The neural network automatically processes sensor data, evaluates multiple targets, and selects optimal targets without human intervention, thereby increasing automation while managing complexity through learned patterns rather than explicit programming
Solution Approach 2:
The system enables the agent to autonomously perform target selection without external human control. The neural network continuously receives sensor inputs, independently evaluates targets based on learned criteria, and automatically generates selection decisions, making the system self-sufficient in its decision-making process
2Adaptability or versatility
If heuristic methods are used for target selection, then the system architecture remains simple, but the system becomes inflexible and misses correlations that machine learning can find
Solution Approach 1:
The patent transforms the target selection system from using fixed heuristic parameters to dynamic parameters learned by the neural network. The network adapts its internal parameters (weights and biases) based on training data, enabling it to capture complex correlations and patterns in the environment that static heuristic rules cannot detect, thereby significantly improving adaptability
Solution Approach 2:
The system transitions from static heuristic rules to a dynamic neural network model that continuously adapts to changing environmental conditions. The network can learn new patterns and adjust its decision-making strategy based on accumulated experience and changing target characteristics, providing flexibility in diverse and evolving operational scenarios
3Productivity
If human operators perform target selection, then the system can handle complex decision-making, but the process becomes time-consuming and less efficient
Solution Approach 1:
The patent replaces the mechanical process of human cognitive decision-making with an automated neural network system. The network processes sensor data and evaluates multiple targets at high speed through parallel computation, eliminating the time delays inherent in human observation, analysis, and decision-making while maintaining or improving decision quality through learned patterns
Solution Approach 2:
The neural network operates continuously without interruption, constantly processing sensor inputs and generating target selection decisions. Unlike human operators who require breaks and have variable processing speeds, the network maintains consistent high-speed operation, maximizing productivity and minimizing idle time in the target selection process
Data Source
AI summary
A system and method for facilitating autonomous target selection through neural networks is disclosed. The method includes receiving a request from a user to select a target from one or more targets for an agent to perform one or more activities and receiving one or more input parameters. The method further includes generating an expected reward value for each of the one or more targets and determining a desired target among the one or more targets based on the generated expected reward or empirical estimation. Further, the method includes generating one or more actions to be executed by the agent corresponding to the desired by using a trained policy network based ANN model and outputting the determined one or more actions to at least one of: the agent and one or more user devices associated with the user for performing the one or more activities.