Difference between revisions of "Agents"

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* [[Development]]  ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]]  ... [[Visualization]]  ... [[Diagrams for Business Analysis]]
 
* [[Development]]  ...[[Development#AI Pair Programming Tools|AI Pair Programming Tools]] ... [[Analytics]]  ... [[Visualization]]  ... [[Diagrams for Business Analysis]]
 
* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
 
* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
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* [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ...[[Large Language Model (LLM)|LLM]]  ...[[Natural Language Tools & Services|Tools & Services]]
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* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 
* [[Attention]] Mechanism  ...[[Transformer]] Model  ...[[Generative Pre-trained Transformer (GPT)]]
 
* [[Attention]] Mechanism  ...[[Transformer]] Model  ...[[Generative Pre-trained Transformer (GPT)]]
 
* [[Game Theory]]
 
* [[Game Theory]]

Revision as of 16:19, 28 April 2023

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An intelligent agent is anything which perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with learning or acquiring knowledge. An agent has an "objective function" that encapsulates all the IA's goals. Such an agent is designed to create and execute whatever plan will, upon completion, maximize the expected value of the objective function.[2] For example, a reinforcement learning agent has a "reward function" that allows the programmers to shape the IA's desired behavior,[3] and an evolutionary algorithm's behavior is shaped by a "fitness function". - Wikipedia

🤖 Reinforcement Learning (RL) aims to make an agent (our “model”) learn through the interaction with an environment (this can be either virtual or real). RL was firstly developed to adhere to Markov Decision Process (MDP)es. In this ambit, an agent is placed in a stochastic stationary environment and tries to learn a policy through a reward/punishment mechanism. In this scenario, it is proved the agent will converge to a satisfactory policy. However, if multiple agents are placed in the same environment, this condition is no longer true. In fact, before the learning of the agent was only dependent on the interaction between the agent and the environment, now it is also dependent on the interaction between agents


Generative Agents: Interactive Simulacra of Human Behavior

Researchers at Google and Stanford used ChatGPT to generate human-like characters who live and interact in a contained, video game-like world called Smallville. Smallville features 25 characters with preloaded personas who wake up, go to sleep, make breakfast, interact with each other, and attend each other’s parties in an attempt to mimic human behavior as closely as possible. One popular AI observer likened the experiment to an early version of Westworld, though it's more like a video game demo where the characters' actions and dialogue are autogenerated by AI. - Google Tells AI Agents to Behave Like 'Believable Humans' to Create 'Artificial Society' | Chloe Xiang - Vice

Autonomous GPT

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AutoGPT

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AgentGPT

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AgentGPT allows you to configure and deploy Autonomous AI agents. Name your custom AI and have it embark on any goal imaginable. It will attempt to reach the goal by thinking of tasks to do, executing them, and learning from the results

BabyAGI

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AI-Powered Search

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Pinecone

Key concepts:

  • Vector search - Unlike traditional search methods that revolve around keywords, it is done by indexing and searching through ML-generated representations of data — vector embeddings — to find items most similar to the query.
  • Vector embeddings - Vector embeddings, or “vectors,” are sets of floating-point numbers that represent objects. They are generated by embedding models trained to capture the semantic similarity of objects in a given set.

Multi-Agents Reinforcement Learning (MARL)

In a similar vein, multi-agent RL also addresses sequential decision-making problems, but with more than one agent involved. In particular, both the evolution of the system state and the reward received by each agent are influenced by the joint actions of all agents. More intriguingly, each agent has its own long-term reward to optimize, which now becomes a function of the policies of all other agents.

  • Markov/Stochastic Games
    • Cooperative Setting
    • Competitive Setting
    • Mixed Setting
  • Extensive-Form Games

Challenges

Despite a general model with broad applications, MARL suffers from several challenges in theoretical analysis, in addition to those that arise in single-agent RL.

  1. Non-Unique Learning Goals - Unlike single-agent RL, where the goal of the agent is to maximize the long-term return efficiently, the learning goals of MARL can be vague at times. ...Indeed, the goals that need to be considered in the analysis of MARL algorithms can be multi-dimensional ... is undoubtedly a reasonable solution concept in game theory, under the assumption that the agents are all rational, and are capable of perfectly reasoning and infinite mutual modeling of agents. However, with bounded rationality, the agents may only be able to perform finite mutual modeling
  2. Non-Stationarity - multiple agents usually learn concurrently, causing the environment faced by each individual agent to be non-stationary. In particular, the action taken by one agent affects the reward of other opponent agents, and the evolution of the state. As a result, the learning agent is required to account for how the other agents behave and adapt to the joint behavior accordingly. This invalidates the stationarity assumption for establishing the convergence of single-agent RL algorithms
  3. Scalability Issue - To handle non-stationarity, each individual agent may need to account for the joint action space, whose dimension increases exponentially with the number of agents. This is also referred to as the combinatorial nature of MARL
  4. Various Information Structures - Compared to the single-agent case, the information structure of MARL, namely, who knows what at the training and execution, is more involved. For example, in the framework of Markov games, it suffices to observe the instantaneous state st , in order for each agent to make decisions

Communication

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