Generative AI and Digital twins - Can Agentic RAG implement multiagent simulations?

This is the second part of a two-part post. Part One is here

Generative AI and Digital twins - simulating and modelling complex, cyber physical systems

You should also read the posts linked there from Dirk Hartmann which linked in part one

In Part One, we proposed that 

• Digital twins can ideally model cyber physical systems 
• Digital twins can model complex systems. 
• The exact mechanism to do so is related to simulation.  
• simulation cannot easily handle emergent behaviour 

In this section, we discuss how AI can enhance simulation 

Specifically, we consider how AI can help with incorporating emergent behaviour in simulation.

—-

An emergent behavior is something that is a nonobvious side effect of bringing together a new combination of capabilities—whether related to goods or services. Emergent behaviors can be either beneficial, benign, or potentially harmful, but in all cases they are very difficult to foresee until they manifest themselves. Emergent behaviors are also sometimes considered to be systems that are more complex than the sum of their parts.

An emergent behavior is essentially new, and generated by the combination of two or more different things—none of which displayed the behavior individually. An example of emergent behavior can be found in the insect world, where colonies of simple creatures like ants or termites will form together spontaneously to build very complex structures such as underground colonies or mounds that reach meters into the air, including elaborate cooling ducts and heat-dispersing fins.

Emergent behaviors are also often found in complex systems, both natural (biological) and man-made. For instance, an emergent behavior in nature might arise from the cross-breeding of a species of animal or plant. Suddenly, something new is present in the hybrid that was not present in either of the parents or genetic contributors.

In a man-made system such as software, networks, or the IoT, an emergent behavior might be related to the new creation of an interface, or perhaps the combination of traffic from different services onto a common network. The results of such combinations might be very difficult to assess, because the behavior is not predictable based on the known behaviors of the source systems.

—-- source emergent behaviour i

There are already interesting areas in digital twins, generative AI  and simulation 

Multiagent simulation with autogen

Nvidia omniverse has been combining generative AI and design

A reference architecture for digital twins  

The focus for AI today  is autonomous AI agents.

See this excellent survey on autonomous ai agents  via Aishwarya Naresh Reganti

Autonomous AI agents can be used with simulation and digital twins for  

Real-Time Adaptation and Decision Making

Handling Non-Linearities:  

Multi-Agent Simulations:  

Scenario Exploration:  

The most interesting (and relatively simple) technology for agents today is agentic RAG.  A number of platforms have agentic RAG implementations - specifically llamaindex  

There are very few references to Agentic RAG but this is one more good reference to agentic RAG.  

Agents extend the functionality of the RAG beyond reading data with the ability to dynamically ingest and modify the data. In addition to RAG components, agentic RAG needs a reasoning loop. There are a number of possibilities with llamaindex as per above link:  

1. Function Calling Agents (integrates with any function calling LLM)
2. ReAct agent (works across any chat/text completion endpoint).
3. "Advanced Agents": LLMCompiler, Chain-of-Abstraction, Language Agent Tree Search, and more.

But to model emergent behaviour - there are two questions  

1. Can agentic RAG simulate multiagent behaviour?  
2. Can we use LLMs to create a dynamic schema

Emergent behaviour needs multiple agents but modelling multiagent simulations is complex,  Can agentic RAG systems model multi agent simulation systems?   

I believe that this is possible.  

Agentic Retrieval-Augmented Generation (RAG) can be used to implement multi-agent simulations by defining distinct agents with specific roles and knowledge bases. Each agent retrieves relevant information from a shared or separate corpus, updating dynamically based on interactions. The generative model uses this information to produce contextually appropriate actions and decisions. The agency component introduces decision-making and adaptability, allowing agents to adjust their behavior based on real-time feedback, enhancing the realism and complexity of the simulation.

Agentic Retrieval-Augmented Generation (RAG) can be applied to various multi-agent simulations. In smart city simulations, it helps optimize urban planning and management by simulating interactions between traffic systems, public transportation, law enforcement, and citizens. For economic models, it can study market dynamics and policy impacts by modeling interactions between consumers, businesses, and regulators.

Additionally, environmental simulations can benefit from RAG by simulating ecosystems with various species and their interactions, allowing the study of environmental changes. Social dynamics simulations can model social interactions within a community to study behaviors, social norms, and the spread of information or diseases. 

The second aspect using LLMs to build schemas dynamically for digital twins ex  reference architecture for digital twins  . We are still exploring this area. 

Comments welcome