The space of possible GPT-4 outputs is hard to comprehend.
The space of possible different "graphs" of LLM agents connected to each other is even larger.
Each graph represents a multiagent system
Here's a generic syntax for notating graphs that don't have loops (essentially trees):
AgentName: Descriptive Name
Goals:
- Goal1
- Goal2
...
Techniques:
- Instruction1
- Instruction2
...
Inputs:
- From AgentName: Description of input
- From OtherAgentName: Description of input
...
Outputs:
- To AgentName: Description of output
- To OtherAgentName: Description of output
...
-> SubAgentName1: Descriptive Name
Goals:
- Goal1
- Goal2
...
Techniques:
- Instruction1
- Instruction2
...
Inputs:
- From AgentName: Description of input
- From OtherAgentName: Description of input
...
Outputs:
- To AgentName: Description of output
- To OtherAgentName: Description of output
...
-> SubSubAgentName1: Descriptive Name
Goals:
- Goal1
- Goal2
Techniques:
- Instruction1
- Instruction2
Inputs:
- From SubAgentName1: Description of input
Outputs:
- To SubAgentName1: Description of output
...
-> SubSubAgentName2: Descriptive Name
Goals:
- Goal1
- Goal2
Techniques:
- Instruction1
- Instruction2
Inputs:
- From SubAgentName1: Description of input
Outputs:
- To SubAgentName1: Description of output
...
-> SubAgentName2: Descriptive Name
Goals:
- Goal1
- Goal2
...
Techniques:
- Instruction1
- Instruction2
...
Inputs:
- From AgentName: Description of input
...
Outputs:
- To AgentName: Description of output
...
---
Here's an example researcher agent and it's interior using the syntax. The English translation was lost in my notes but you can put this to a translator:
Tutkimus:
Tavoitteet:
- Tuottaa ja analysoida tiedusteluja
- Rakentaa uutta tutkimusta
- Tutkia tuntemattomia aiheita
- Päätellä tiedusteluista
Tekniset ohjeet:
- Ohje / sääntö päättelylle 1
- Ohje / sääntö päättelylle 2
Syötteet:
- Agentilta Meta-tietoisuus: Ehdotukset
- Agentilta Alatutkimus: Tulokset
Tulosteet:
- Agentille Alatutkimus: Käskyt
- Agentille Muisti: Tutkimus
-> Muisti:
Tavoitteet:
- Ylläpitää tutkimuksen tallennetta
Tekniikat:
- Ohje / sääntö muistikantojen käyttämiselle 1
- Ohje / sääntö muistikantojen käyttämiselle 2
Syötteet:
- Agentilta Tutkimus: Tutkimus
Tulosteet: Ei mitään
---
Signal theory becomes relevant when thinking about I/O, embedded agency, and when the agents aren't / cannot be constantly "reading" each other.
---
For similar projects, the current AutoGPT-style systems are very primitive and haven't adapted to my ideas. If what I call the cognitive architectures of the LLM-multiagent systems were carefully designed, which I predict will become a thing (and subject to ton of future research!), our AI systems could gain very advanced cognitive capabilities, perhaps even approaching humans but in their own, formal manner.
I appreciate the detailed response. I'll look into the SocraticAI project as well.
FWIW I asked because I'm working on a toolkit for applying Monte Carlo tree search to agent graph generation and am always on the lookout for fundamental insights that could help direct its development.
The space of possible different "graphs" of LLM agents connected to each other is even larger.
Each graph represents a multiagent system
Here's a generic syntax for notating graphs that don't have loops (essentially trees):
AgentName: Descriptive Name Goals: - Goal1 - Goal2 ...
Techniques: - Instruction1 - Instruction2 ...
Inputs: - From AgentName: Description of input - From OtherAgentName: Description of input ...
Outputs: - To AgentName: Description of output - To OtherAgentName: Description of output ...
-> SubAgentName1: Descriptive Name Goals: - Goal1 - Goal2 ...
Techniques: - Instruction1 - Instruction2 ...
Inputs: - From AgentName: Description of input - From OtherAgentName: Description of input ...
Outputs: - To AgentName: Description of output - To OtherAgentName: Description of output ...
-> SubAgentName2: Descriptive Name Goals: - Goal1 - Goal2 ...Techniques: - Instruction1 - Instruction2 ...
Inputs: - From AgentName: Description of input ...
Outputs: - To AgentName: Description of output ...
---
Here's an example researcher agent and it's interior using the syntax. The English translation was lost in my notes but you can put this to a translator:
Tutkimus: Tavoitteet: - Tuottaa ja analysoida tiedusteluja - Rakentaa uutta tutkimusta - Tutkia tuntemattomia aiheita - Päätellä tiedusteluista Tekniset ohjeet: - Ohje / sääntö päättelylle 1 - Ohje / sääntö päättelylle 2 Syötteet: - Agentilta Meta-tietoisuus: Ehdotukset - Agentilta Alatutkimus: Tulokset Tulosteet: - Agentille Alatutkimus: Käskyt - Agentille Muisti: Tutkimus
-> Alatutkimus: Tavoitteet: - Suorittaa erityisiä tutkimustehtäviä Tekniset ohjeet: - Ohje / sääntö käskyjen noudattamiselle 1 - Ohje / sääntö käskyjen noudattamiselle 2 Syötteet: - Agentilta Tutkimus: Käskyt Tulosteet: - Agentille Tutkimus: Tulokset
-> Muisti: Tavoitteet: - Ylläpitää tutkimuksen tallennetta Tekniikat: - Ohje / sääntö muistikantojen käyttämiselle 1 - Ohje / sääntö muistikantojen käyttämiselle 2 Syötteet: - Agentilta Tutkimus: Tutkimus Tulosteet: Ei mitään
---
Signal theory becomes relevant when thinking about I/O, embedded agency, and when the agents aren't / cannot be constantly "reading" each other.
---
For similar projects, the current AutoGPT-style systems are very primitive and haven't adapted to my ideas. If what I call the cognitive architectures of the LLM-multiagent systems were carefully designed, which I predict will become a thing (and subject to ton of future research!), our AI systems could gain very advanced cognitive capabilities, perhaps even approaching humans but in their own, formal manner.
One person suggested me this:
https://princeton-nlp.github.io/SocraticAI/
I haven't read it but seems to have similarities.