Autonomous Action Planning With Candidate Objectives for Abnormal Events
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Solution Overview
Problem
Conventional autonomous systems face challenges in reacting to unexpected events, experiencing long computation times for action strategy establishment, and employing static strategies that are unsuitable for dynamic environments, leading to potential interruptions and inefficiencies.
Innovation Solution
The development of a method for generating and executing adaptive action strategies by an autonomous system, which involves accessing databases of event descriptions and candidate objectives, selecting appropriate objectives based on activation conditions, and executing a series of actions through progressive task unit structures that include failure recovery modules, allowing for real-time adjustments and stochastic decision processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional autonomous systems use static action strategies, then system simplicity is maintained, but the system cannot adapt to unexpected events or dynamic environments
Solution Approach 1:
The action strategy is segmented into multiple candidate objectives, each with its own progressive task unit structure. This allows the system to evaluate multiple pre-defined strategies and select the most appropriate one for the current situation, enabling adaptability without requiring a completely complex dynamic generation system.
Solution Approach 2:
Multiple candidate objectives and their corresponding progressive task unit structures are prepared in advance. When an unexpected event occurs, the system can quickly select from these pre-prepared strategies rather than generating them in real-time, reducing computation time while maintaining adaptability.
2Reliability
If conventional systems perform comprehensive computation for action strategy establishment, then optimal strategies are achieved, but computation time becomes excessively long causing interruptions
Solution Approach 1:
Candidate objectives and progressive task unit structures are computed and prepared in advance offline. During online operation, the system only needs to evaluate which pre-computed strategy is most appropriate for the current event, dramatically reducing computation time while maintaining strategy quality.
Solution Approach 2:
The comprehensive action strategy is divided into multiple candidate objectives with progressive task unit structures. This segmentation allows the system to evaluate smaller, manageable strategy components rather than computing one large strategy from scratch, reducing online computation time.
3Reliability
If autonomous systems use detailed progressive task unit structures with failure recovery modules, then task accomplishment reliability is improved, but device complexity increases
Solution Approach 1:
The task unit structure is segmented into hierarchical levels with clear progression. Each level has specific sub-tasks and failure recovery modules, allowing the system to handle complexity in a structured, manageable way while maintaining high reliability through progressive validation at each level.
Solution Approach 2:
Failure recovery modules are built into the progressive task unit structure in advance. When failures occur, the system can immediately execute pre-planned recovery actions without needing to compute new strategies, ensuring task accomplishment reliability while keeping the overall system manageable.
4Speed
If autonomous systems implement real-time event detection and response mechanisms, then responsiveness to abnormal events is improved, but computation overhead increases
Solution Approach 1:
Event detection rules and candidate objective activation conditions are established in advance. When events occur, the system only needs to check whether pre-defined activation conditions are met, rather than performing comprehensive analysis, significantly reducing real-time computation energy while maintaining fast response.
Data Source
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AI summary
Systems and methods for generating an action strategy to be executed by an autonomous system are disclosed. The action strategy comprises a series of actions to be performed by the autonomous system to accomplish a corresponding active objective in response to detecting an abnormal event, the abnormal events occurring or having occurred in an environment where the autonomous system is configured to operate. The method comprises accessing a first database populated with event descriptions corresponding to abnormal events and accessing a second database populated with candidate objectives. Each candidate objective defines a task accomplishable by the autonomous system and comprises an activation condition and a progressive task unit structure describing a hierarchy of actions to be performed in order to accomplish the corresponding candidate objective. An execution of a candidate objective generating an action strategy from the progressive task unit structure of the active objective and executing the action strategy.