https://github.com/tegridydev/ses-dma
SES-DMA | (Self-Evolving System with Dynamic Memory Architecture)
https://github.com/tegridydev/ses-dma
Last synced: 4 months ago
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SES-DMA | (Self-Evolving System with Dynamic Memory Architecture)
- Host: GitHub
- URL: https://github.com/tegridydev/ses-dma
- Owner: tegridydev
- Created: 2024-11-17T10:38:00.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T11:14:08.000Z (6 months ago)
- Last Synced: 2024-12-05T14:09:30.144Z (6 months ago)
- Language: Python
- Size: 12.7 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Title:
SES-DMA: A Self-Evolving System with Dynamic Memory Architecture for Distributed Multi-Agent Learning
## Abstract:
This paper presents a novel architecture for distributed learning systems that combines mixture of peers (MoP) methodology with dynamic memory allocation and self-evolution capabilities. The SES-DMA system introduces a unique approach to managing distributed learning agents while maintaining system-wide knowledge coherence and progressive learning capabilities. We demonstrate the system's effectiveness through empirical evaluation across multiple learning tasks and computational environments.
## Research Questions:
1. How does the MoP architecture affect the system's learning efficiency compared to traditional single-agent systems?
2. What is the impact of dynamic memory architecture on knowledge retention and retrieval?
3. How does the self-evolution mechanism contribute to system adaptation and performance improvement?
4. What are the scalability characteristics of the SES-DMA system under varying load conditions?
5. How does the system maintain consistency across distributed agents while allowing for specialized learning?
## Hypotheses:
H1: The MoP architecture provides superior learning performance compared to single-agent systems due to specialized agent roles and collaborative learning.
H2: Dynamic memory architecture significantly improves knowledge retention and retrieval efficiency compared to static memory systems.
H3: Self-evolution mechanisms lead to measurable improvements in system performance over time without human intervention.
H4: The distributed nature of SES-DMA provides linear scalability up to a certain threshold of computational resources.
## Methodology:
1. System Implementation:
- Development of core MoP architecture
- Implementation of dynamic memory system
- Creation of evolution mechanisms
- Integration of distributed computing capabilities
2. Experimental Design:
- Benchmark tasks for system evaluation
- Performance metrics definition
- Scalability testing parameters
- Comparative analysis framework
3. Evaluation Metrics:
- Learning efficiency (time to convergence)
- Memory utilization and retrieval speed
- Adaptation rate to new tasks
- Resource utilization efficiency
- System coherence measures
## Planned Experiments:
1. Baseline Performance Testing:
- Single agent vs MoP architecture
- Static vs dynamic memory
- With and without evolution mechanisms
2. Scalability Testing:
- Varying number of agents
- Increasing task complexity
- Resource utilization analysis
3. Long-term Evolution Analysis:
- System performance over extended periods
- Adaptation to changing conditions
- Knowledge retention and pruning efficiency
4. Distributed Computing Effects:
- Network latency impact
- Resource allocation efficiency
- System coordination overhead