Concurrent Multi-Agent BDI Architecture with Domain Migration
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Solution Overview
Problem
Current BDI architectures for complex systems lack effective modeling of concurrent processes, such as opportunity detection, planning, reactive reconsideration, and intention persistence, and often rely on sequential algorithms that do not accurately represent the dynamic nature of desires, beliefs, and intentions, limiting their ability to efficiently control complex systems in real-time and adapt to changing environments.
Innovation Solution
A system employing a concurrent multi-agent BDI architecture with domain migration capabilities, featuring BDI agent modules that include beliefs, goal, and means-end managers, allowing for real-time control and adaptation through concurrent communication and dynamic domain migration between hardware, software, and hybrid implementations, enabling high parallelism and efficient resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If sequential algorithms are used to model BDI architecture, then implementation simplicity is improved, but the ability to accurately represent dynamic concurrent processes deteriorates
Solution Approach 1:
The patent transitions from static sequential algorithms to dynamic concurrent processes that can adapt and respond in real-time. The BDI architecture is restructured to allow simultaneous execution of belief updates, desire evaluations, and intention formations, accurately capturing the dynamic nature of autonomous agent behavior.
Solution Approach 2:
The system is divided into independent concurrent process modules (belief processing, desire evaluation, intention formation) that can execute simultaneously. This segmentation allows each component to operate independently while contributing to the overall agent behavior, improving both accuracy and parallelism.
2Productivity
If concurrent processes are properly modeled in BDI architecture, then the ability to control complex systems in real-time is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal concurrent BDI framework that can handle multiple types of agent interactions and complex system controls through a unified architecture. This multi-functional design allows the same core mechanisms to manage diverse concurrent processes, reducing the need for separate specialized components.
Solution Approach 2:
The system adds a temporal dimension to BDI processing by enabling simultaneous execution across multiple time steps. Concurrent processes operate in parallel time dimensions rather than sequential steps, allowing real-time control of complex systems without linearly increasing processing complexity.
3Adaptability or versatility
If domain migration between hardware, software, and hybrid is implemented, then adaptability and resource utilization are improved, but implementation complexity increases
Solution Approach 1:
The patent implements dynamic domain migration capabilities that allow BDI agents to transition between hardware, software, and hybrid domains based on runtime requirements. This dynamic adaptability enables optimal resource utilization while maintaining a unified architectural framework that manages complexity through standardized migration protocols.
Data Source
AI summary
Systems and methods for controlling and implementing a concurrent multi-agent BDI architecture to control complex systems in real time and exhibiting high parallelism. The system comprises BDI agent modules storing a BDI state of the whole system, an emotional and an internal the BDI agent modules and a world model. Each BDI agent module concurrently evaluates the activation of its potential goals and prioritizes its intentions. Each BDI agent module may be hardware-implemented, software-implemented or hybrid-domain-implemented, depending on whether they are executed on application-specific hardware, general purpose hardware or a combination thereof. The domain of each BDI agent might be migrated in real time during operation, optimizing resources and improving performance.