Spatiotemporal Planning System Using Heuristic Search
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Solution Overview
Problem
Current artificial intelligence planning systems lack native support for spatiotemporal planning problems, making it difficult to model and solve complex planning issues involving spatial and temporal aspects, especially in mobile solutions, where traditional methods are inadequate and require extensive scientific expertise.
Innovation Solution
The implementation of an object-oriented programming approach using representation state transfer (REST) and Java-based libraries and APIs to provide native support for spatiotemporal planning, allowing developers to focus on domain-specific aspects by automatically handling spatial and temporal aspects through methods like Move, SwitchLocation, and ReduceLocation, and utilizing heuristic search algorithms such as best-first search and A* algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AI planning methods are used, then existing planning systems can solve basic planning problems, but they lack native support for spatiotemporal planning problems involving spatial and temporal aspects
Solution Approach 1:
The patent segments the planning problem into distinct spatial and temporal components that can be independently modeled and solved. By creating separate domain models for spatial relationships and temporal constraints, the system handles spatiotemporal complexity through modular decomposition rather than monolithic modeling.
Solution Approach 2:
The patent introduces an intermediary layer of abstraction through domain models that mediate between the planning problem and the solving algorithms. These domain models serve as intermediaries that automatically handle spatiotemporal reasoning, allowing developers to focus on high-level planning logic without directly implementing complex spatiotemporal constraints.
2Ease of manufacture
If developers manually model spatiotemporal aspects in mobile solutions, then complete control over planning logic is achieved, but extensive scientific expertise and development time are required
Solution Approach 1:
The patent implements self-service through automated domain models that automatically handle spatiotemporal reasoning without requiring manual intervention. The system autonomously generates and updates domain models based on problem specifications, eliminating the need for developers to manually encode complex spatiotemporal constraints and reducing dependency on specialized scientific expertise.
Solution Approach 2:
The patent performs preliminary action by pre-configuring domain models with spatiotemporal reasoning capabilities before the actual planning task. By establishing the modeling framework and algorithms in advance, the system prepares the infrastructure needed to handle spatiotemporal aspects automatically during runtime, significantly reducing development time for new planning problems.
3Reliability
If iterative informed search is used to solve the domain model, then solutions to complex planning problems can be found, but the process requires multiple iterations and computational resources
Solution Approach 1:
The patent applies partial action by performing informed search only on the necessary portions of the state space rather than exhaustively exploring all possibilities. The domain models guide the search process to focus on relevant spatiotemporal constraints and relationships, performing just enough computational work to find valid solutions without unnecessary iterations through irrelevant state spaces.
Data Source
AI summary
According to one embodiment, a method for solving a plurality of spatiotemporal planning problems. The method may include creating a domain model. The method may also include assigning a solution bound associated with the created domain model to infinity. The method may further include calculating a solution to the created domain model based on the assigned solution bound using at least one of a best-first search algorithm. The method may also include updating the assigned solution bound based on the calculated solution. The method may further include presenting the calculated solution to a user based on the updated solution bound not equaling infinity.