Automated Planning System for Efficient Domain-Specific Goal Achievement
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Solution Overview
Problem
Current automated systems for finding plans in specific domains are inefficient due to their failure to exploit the structure of the domain, often searching through many irrelevant sequences of operators, which makes them unable to solve complex problems in a reasonable time.
Innovation Solution
A system that simulates the domain and its operators to generate and refine candidate plans, focusing only on relevant operators and obstacles, using domain-specific methods to efficiently achieve design goals by ignoring non-affectable obstacles and considering only affectable ones for change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If domain-independent search algorithms are used, then the system can handle any domain, but the search efficiency deteriorates due to exploring irrelevant operators
Solution Approach 1:
The system applies local quality by making the search algorithm adapt to local domain characteristics. Domain-specific knowledge is integrated to identify and prioritize relevant operators and goals for each particular domain, transforming the uniform search approach into a localized one that considers domain-specific structures, relationships, and constraints, thereby improving search efficiency without sacrificing domain independence
Solution Approach 2:
The system implements dynamics by making the search strategy adaptable and flexible rather than static. The planner dynamically adjusts the search process based on domain information, operator relevance, and goal priorities, allowing the system to handle multiple domains effectively while optimizing search efficiency for each specific domain context
2Adaptability or versatility
If domain-independent planners are used, then the system can work with any domain, but the planning time increases due to lack of domain structure exploitation
Solution Approach 1:
The system applies preliminary action by performing domain analysis and operator classification before the actual planning process. The system pre-identifies relevant operators, goals, and domain structures, and prepares domain-specific knowledge bases in advance, so that during planning only the necessary operators are considered, significantly reducing planning time while maintaining domain independence
Solution Approach 2:
The system makes the planning process locally adapted to each domain by incorporating domain-specific knowledge about operator relevance, goal priorities, and structural relationships. This localized planning approach reduces the search space by focusing on domain-relevant operators and patterns, decreasing planning time without limiting domain applicability
3Reliability
If all operators are considered in the search, then completeness is maintained, but the search space becomes too large to solve problems in reasonable time
Solution Approach 1:
The system applies the extraction principle by separating relevant operators from irrelevant ones based on domain knowledge and goal analysis. The system extracts and prioritizes operators that are actually relevant to achieving the current goals, removing unnecessary operators from the search space, thereby maintaining completeness for relevant solutions while dramatically improving problem-solving speed
Solution Approach 2:
The system implements partial action by considering only the necessary subset of operators required to solve the problem, rather than exhaustively searching all possible operators. The system identifies and focuses on the critical operators that lead to goal achievement, achieving sufficient completeness for practical purposes while significantly reducing search space and improving efficiency
4Adaptability or versatility
If non-affectable obstacles are included in the plan, then the plan appears more comprehensive, but the plan cannot be executed since these obstacles cannot be changed
Solution Approach 1:
The system applies extraction by separating affectable obstacles from non-affectable obstacles and handling them differently. Non-affectable obstacles are identified and excluded from the actionable plan, while affectable obstacles are included with specific operations to change them. This ensures the plan remains comprehensive in identifying all obstacles but executable by focusing only on those that can be modified
Solution Approach 2:
The system inverts the traditional approach by not treating all obstacles equally. Instead of attempting to address every obstacle, the system inverts the strategy by explicitly identifying and excluding non-affectable obstacles from the execution plan, focusing resources only on affectable obstacles that can actually be changed, thereby improving plan executability while maintaining comprehensive analysis
Data Source
AI summary
A system for achieving a desired goal in a domain. The system may comprise a device operable to receive information and simulate the domain therefrom; a device operable to simulate one or more effects due to one or more operators; a device operable to specify a number of items and/or classes of items and whether each item and/or each class of items is an affectable obstacle or a non-affectable obstacle; a device operable to automatically generate a candidate plan to achieve the desired goal by utilizing the simulated domain and the simulated effect(s), wherein the candidate plan could involve one or more affectable obstacles but does not involve any non-affectable obstacles; and a device operable to automatically refine the candidate plan to change at least one of the affectable obstacles involved in the candidate plan.