Temporal Constraint Network for Activity Planning
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Solution Overview
Problem
Current distributed computer systems lack effective methods for actively planning activities that incorporate temporal constraints, such as scheduling events and determining the likelihood of satisfying time-based requirements in queries.
Innovation Solution
An active activity planning system comprising a knowledge base, query processor, and temporal reasoner that converts queries with temporal constraints into abstract temporal constraint networks, allowing for the computation of likelihood scores and generation of activity plans that satisfy these constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If queries with temporal constraints are processed using conventional methods, then the system can handle basic queries, but it cannot effectively compute likelihood scores or generate activity plans that satisfy temporal requirements
Solution Approach 1:
The system segments the complex temporal reasoning task into distinct functional modules: a query processor that converts natural language queries into formal representations, a temporal reasoner that computes likelihood scores, and a plan generator that creates activity plans. This segmentation allows each component to specialize in specific aspects of temporal constraint handling, improving reliability without overwhelming system complexity.
Solution Approach 2:
The patent introduces an intermediary formal representation layer (temporal constraint networks) that bridges the gap between natural language queries and computational processing. This intermediate structure enables precise temporal reasoning by translating ambiguous temporal expressions into structured constraints that can be systematically evaluated for likelihood scores.
2Measurement precision
If the system converts queries into formal temporal constraint networks, then it can compute likelihood scores accurately, but the processing complexity increases
Solution Approach 1:
The system transforms temporal constraints into parameterized formal representations with specific attributes (timestamps, durations, intervals). By changing the parameter structure from natural language to formal temporal parameters, the system enables precise likelihood score computation through mathematical evaluation of constraint satisfaction probabilities.
Solution Approach 2:
The patent replaces intuitive but imprecise temporal reasoning with a formal computational mechanism. Instead of relying on natural language interpretation, the system uses structured temporal constraint networks with defined mathematical properties, allowing algorithmic computation of likelihood scores through systematic evaluation of temporal relationships.
3Manufacturing precision
If the system generates detailed activity plans with temporal constraints, then it meets user time requirements, but the computational time and resources increase
Solution Approach 1:
The system performs preliminary conversion of natural language queries into formal temporal constraint networks before generating activity plans. This preliminary structuring of temporal requirements enables more efficient plan generation by pre-establishing the temporal framework within which activities must be scheduled, reducing computational time during the actual planning phase.
Solution Approach 2:
The temporal constraint network uses dynamic parameter representation that can adapt during plan generation. The system dynamically evaluates likelihood scores and adjusts activity plans based on temporal constraint satisfaction, allowing efficient exploration of feasible schedules while maintaining high accuracy in meeting time requirements.
Data Source
AI summary
An activity planning system comprises a knowledge base, a query processor, and a temporal reasoner. A query including temporal constraints is input into the query processor. The query processor converts the query into a formal representation. The formal representation is a formal graphical semantic representation grounded on an ontology defined in the knowledge base. The temporal reasoner processes the query representation output by the query processor against the knowledge base which defines a set of object. For each object, the temporal reasoner produces a normalized score from 0 to 1 to indicate the degree of how likely the object satisfies the temporal constraints imposed by the query.