Discord: https://bit.ly/SECoursesDiscord. So many people were dreaming of NPC agents powered by ChatGPT. Here the first model of such games. If I have been of assistance to you and you would like to show your support for my work, please consider becoming a patron on 🥰 https://www.patreon.com/SECourses

This game is like playing The Sims against real people not NPCs.

Scientific Paper PDF ⤵️
https://arxiv.org/pdf/2304.03442.pdf

Pre-recorded demo of the game ⤵️
https://reverie.herokuapp.com/arXiv_Demo/

Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️
https://www.youtube.com/playlist?list=PL_pbwdIyffsnkay6X91BWb9rrfLATUMr3

Playlist of StableDiffusion Tutorials, Automatic1111 and Google Colab Guides, DreamBooth, Textual Inversion / Embedding, LoRA, AI Upscaling, Pix2Pix, Img2Img ⤵️
https://www.youtube.com/playlist?list=PL_pbwdIyffsmclLl0O144nQRnezKlNdx3

0:00 Introduction to ChatGPT powered agents having first ever made RPG game Smallville
0:48 What is Smallville SandBox RPG Game
1:12 The community of the ChatGPT powered Smallville game
2:05 Core concept of the run by the GPT engine Smallville game
2:09 Inter-agent communication
3:27 User control
3:40 Environmental interaction
4:08 Smallville sandbox environment implementation
4:23 Example day in life of ChatGPT powered agents
4:42 Why the game is still far away from being a full-fledged game
5:11 Each day a new plan is made
5:40 Emerging social behaviors
5:56 Information diffusion
6:20 Relationship memory
6:53 The hardest part of such ChatGPT powered agents having RPG game making
7:25 Workflow of the Smallville game
8:12 Generative agent architecture
8:59 Memory and retrieval – the core of the game
10:10 Memory stream architecture
14:02 Reflection memory
15:24 Planning and reaction
16:53 Reacting and updating plans
17:41 From structured world environments to natural language
18:12 Human role playing evaluation
18:41 The problems of the ChatGPT powered game
19:08 Ethics and societal impact
20:14 Demo of the game

CCS CONCEPTS
• Human-centered computing→Interactive systems and tools;
• Computing methodologies → Natural language processing.
KEYWORDS
Human-AI Interaction, agents, generative AI, large language models

Generative Agents: Interactive Simulacra of Human Behavior
Generative agents create believable simulacra of human behavior for interactive applications. In this work, we demonstrate generative agents by populating a sandbox environment, reminiscent of The Sims, with twenty-five agents. Users can observe and intervene as agents they plan their days, share news, form relationships, and coordinate group activities.

The Smallville sandbox world, with areas labeled. The root node describes the entire world, children describe areas (e.g., houses, cafe, stores), and leaf nodes describe objects (e.g., table, bookshelf). Agent remember a subgraph reflecting the parts of the world they have seen, in the state that they saw them.

A morning in the life of a generative agent, John Lin. John wakes up around 6 am and completes his morning routine, which includes brushing his teeth, taking a shower, and eating breakfast. He briefly catches up with his wife, Mei, and son, Eddy, before heading out to begin his workday.

At the beginning of the simulation, one agent is initialized with an intent to organize a Valentine’s Day party. Despite many possible points of failure in the ensuring chain of events—agents might not act on that intent, might not remember to tell others, might not remember to show
up—the Valentine’s Day party does in fact occur, with a number of agents gathering and interacting.

Generative agent architecture. Agents perceive their environment, and all perceptions are saved in a comprehensive record of the agent’s experiences called the memory stream. Based on their perceptions, the architecture retrieves relevant memories, then uses those retrieved actions to determine an action. These retrieved memories are also used to form longer-term plans, and to create higher-level reflections, which are both entered into the memory stream for future use.

The memory stream comprises a large number of observations that are relevant and irrelevant to the agent’s current situation. Retrieval identifies a subset of these observations that should be passed to the language model to condition its response to the situation.

A reflection tree for Klaus Mueller. The agent’s observations of the world, represented in the leaf nodes, are recursively synthesized to derive Klaus’s self-notion that he is highly dedicated to his research.

CONCLUSION
This paper introduces generative agents, interactive computational agents that simulate human behavior. We describe an architecture for generative agents that provides a mechanism for storing
a comprehensive record of an agent’s experiences, deepening its understanding of itself and the environment through reflection, and retrieving a compact subset of that information.

Source

ClicGo Demo