Collaborative Cognition Platform for Dynamic Human-Machine Agent Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing social machines are limited in functionality and adaptability as they are typically created for specific and static tasks, restricting the potential for dynamic collaboration between human and machine-based agents.
Innovation Solution
A collaborative cognition platform that enables the creation and hosting of social machines by integrating human and machine-based agents with algorithms and rules, facilitating systematic iterations of collaboration through collaborative learning and online intrinsic learning, allowing for adaptive task execution and output of collaborative resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social machines are created for specific and static tasks, then task execution is simplified and controlled, but functionality and adaptability are limited
Solution Approach 1:
The social machine platform enables a single system to perform multiple different collaborative tasks by dynamically configuring agents, algorithms, and rules. The platform supports diverse task types including collaborative decision-making, problem-solving, and information gathering, allowing the same infrastructure to adapt to various functional requirements without being dedicated to a single static purpose
Solution Approach 2:
The system transitions from static task configurations to dynamic, iterative collaboration processes. Agents can revise opinions across multiple iterations, learn from external signals, and adapt their behaviors based on collaborative interactions. The platform enables continuous evolution of task execution through systematic iterations rather than fixed one-time operations
2Reliability
If systematic iterations of collaboration are implemented with learning mechanisms, then collaborative resolutions are enhanced, but computational complexity increases
Solution Approach 1:
The system implements feedback loops where agents receive signals from external sources and from other agents' opinions, then revise their own opinions in subsequent iterations. This feedback mechanism enables collaborative learning and improves the quality of resolutions by incorporating multiple perspectives and external information sources across iterative cycles
Solution Approach 2:
Agents autonomously revise their own opinions based on received signals and peer influence without requiring centralized control for each decision. The learning mechanisms operate automatically through the collaborative process, with agents self-updating their positions based on the defined algorithms and rules, reducing the need for complex external management
Data Source
AI summary
Methods, systems, and computer program products for a collaborative cognition platform for creating and hosting social machines are provided herein. A computer-implemented method includes creating a social machine for collaborative tasks, wherein the social machine comprises (i) one or more human agents, (ii) one or more machine-based agents, (iii) an algorithm, and (iv) a set of rules prescribed for executing the collaborative tasks. The method also includes generating one or more collaborative resolutions for the collaborative tasks by executing, in an automated fashion, the collaborative tasks via implementation of the algorithm, wherein the algorithm facilitates, in accordance with the set of rules, systematic iterations of collaboration among (i) the one or more human agents and (ii) the one or more machine-based agents. Further, the method includes outputting the one or more collaborative resolutions to at least one user.