Distributed Reasoning Architecture for Autonomous Team Planning
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Solution Overview
Problem
Existing autonomous systems face challenges in efficient operation and resource management, particularly when working in teams, as they often rely on swarming methods that result in resource wastage and require customized algorithms for each new platform, making development and testing difficult.
Innovation Solution
Implementing a distributed reasoning model that allows team members to share team goals and derive individual self-goals, enabling independent but collaborative actions that support overall team objectives, using a system comprising an autonomous reasoning engine, platform control system, and platform-specific module to execute hierarchical plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If swarming methods are used for team operation, then autonomous systems can work together, but resource wastage occurs and customized algorithms are required for each platform
Solution Approach 1:
The autonomous reasoning engine is segmented into distinct subsystems (beliefs processing, events processing, intentions processing, planning) that can independently handle specific tasks. This segmentation allows each subsystem to operate efficiently with dedicated resources, reducing overall resource wastage while maintaining team operation capabilities through standardized interfaces.
Solution Approach 2:
The BDI planning framework provides a universal architecture that can be applied across different autonomous system platforms without requiring customized algorithms for each platform. The standardized belief-event-intention-plan structure enables multi-platform compatibility and team collaboration while avoiding the resource expenditure of developing platform-specific solutions.
2Adaptability or versatility
If swarming methods are used for team operation, then autonomous systems can work together, but customized algorithms are required for each new platform
Solution Approach 1:
The BDI planning framework serves as a universal architecture that handles team operation logic in a platform-agnostic manner. By standardizing how beliefs, events, intentions, and plans are processed, the system eliminates the need for customized algorithms for each new platform, reducing development and testing complexity while maintaining adaptability across different autonomous systems.
Solution Approach 2:
The autonomous reasoning engine acts as an intermediary layer between the platform control system and team operation requirements. This intermediary translates diverse platform-specific operations into a standardized BDI framework, allowing team collaboration without requiring each platform to implement custom algorithms, thereby reducing overall system complexity.
3Loss of energy
If distributed reasoning model is implemented, then resource efficiency improves, but system complexity increases
Solution Approach 1:
The system is divided into specialized subsystems (beliefs processing, events processing, intentions processing, planning) that each handle specific cognitive functions. This segmentation improves resource efficiency by dedicating computational resources to specific tasks while managing complexity through modular design with well-defined interfaces between subsystems.
Solution Approach 2:
Each autonomous system member independently processes its own beliefs, events, intentions, and plans using the BDI framework, enabling self-service operation that improves resource efficiency. The standardized framework provides self-organizing capabilities that manage system complexity automatically without requiring centralized control, allowing distributed reasoning to scale efficiently.
Data Source
AI summary
What is disclosed is a system to implement distributed reasoning for autonomous operation in a team of members, wherein the team of members comprise a first member, wherein the first member comprises a first team autonomous subsystem and first member autonomous subsystem coupled to each other, the first member executes a first hierarchical plan comprising a team plan and a first member plan, wherein the team plan is related to one or more team goals, and the first member plan is related to one or more self-goals for the first member, further wherein the execution comprises the first team autonomous system executing the team plan, and the first member autonomous system executing the first member plan.