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