Cognitive Guide Engine Real-Time Route Optimization
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Solution Overview
Problem
Current multi-objective optimization techniques in cognitive computing struggle to adapt in real-time to dynamic environments and participant preferences during excursions, failing to efficiently balance conflicting objectives such as minimizing expenses and maximizing enjoyment.
Innovation Solution
A cognitive guide engine collects real-time sensory data from participants and environments, using multi-objective optimization to dynamically adjust routes, regroup participants based on preferences, and notify them of new routes that optimize both objectives, thereby enhancing the excursion experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-objective optimization techniques are used to balance conflicting objectives (minimizing expenses and maximizing enjoyment), then the quality of excursion planning is improved, but the system cannot adapt in real-time to dynamic environments and participant preferences
Solution Approach 1:
The system dynamically adjusts excursion routes by continuously collecting real-time sensory data from cognitive agents and participants, then re-optimizing routes based on current conditions rather than using static pre-planned routes. This enables the system to adapt to changing environments and participant preferences while maintaining optimization efficiency through incremental adjustments.
Solution Approach 2:
The system implements continuous feedback loops where cognitive agents collect sensory data about participant locations, pace, and interest levels, which is fed back to the optimization engine. This feedback mechanism enables real-time adaptation by allowing the system to respond to actual participant behavior and environmental conditions, resolving the contradiction between adaptability and efficiency.
2Ease of operation
If a single unified route is provided to all participants, then the system complexity is reduced, but it cannot personalize the excursion experience to individual participant preferences and pacing
Solution Approach 1:
The system segments participants into different groups based on their preferences, pace, and interest levels detected by cognitive agents. Each segment receives customized route recommendations tailored to their characteristics, enabling personalization while maintaining operational simplicity through automated grouping and template-based route generation for each segment.
Solution Approach 2:
The system applies different route characteristics and optimization criteria to different participant segments based on their local needs and preferences. Each group receives routes with locally optimized qualities (e.g., pace, landmarks, duration) rather than a single uniform route, achieving personalization without significantly increasing overall system complexity.
3Measurement precision
If real-time sensory data collection from all participants is implemented, then the accuracy of optimization is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system introduces cognitive agents as intermediary devices that collect sensory data about participants and environments. These agents act as mediators between participants and the central optimization system, reducing direct complexity by localizing data collection functions and processing information before transmission to the central engine, thereby enabling accurate measurement without proportionally increasing overall system complexity.
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: providing a cognitive guide service to a group of participants for an excursion with an initial route planned by participants registration information and environment information along the initial route. During the excursion, real time sensory data on the participants and change in environment are collected by a cognitive agent accompanying the group to lead the excursion are relayed to a cognitive guide engine, and real time multi-objective optimization is modeled and performed. The participants are regrouped responsive to objectives of the excursion as represented by respective levels of interest in certain stage of the excursion as well as circumstances of the environment. Respective subgroups are formed from the participants per respective objectives, and respective new routes are selected from a set of optimal solutions for each subgroup. During the excursion, the cognitive guide engine iteratively optimizes routes responsive to incoming real time sensory data and objectives of the excursion.