PANDAI
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  • Introduction
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  • Tecnical Documents
    • ERC-6551
    • LLM
      • Key Concepts
      • AI Agent
      • Memory System
      • Emotion System
    • Explanation of AI's Various Brain Region Modules
      • Human-like AI Characteristics
      • Agent Basic Attributes
      • Agent State Description
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  2. Explanation of AI's Various Brain Region Modules

Agent State Description

PreviousAgent Basic Attributes

Last updated 1 year ago

Active State Transition Process

Explanation of Various State Modules

State Name
State Description
Decision Basis

Perspect

In this state, an agent's tasks include:

  1. Analyzing observations of the external world, such as discovering it is in a restaurant and analyzing the layout, menu, and other elements.

  2. Analyzing its internal state, including feelings of fullness (whether it is hungry), health level, energy level, and mood.

  3. Analyzing the consistency between its current behavior and plans.

  • Current date

  • Current location

  • Observations of the world, including the building the agent is in and the people around it

  • The agent's internal state

  • The agent's current plan

  • The agent's understanding and interpretation of the world

Plan

In this state, the agent will anticipate and plan future actions, specifically including:

  • Setting short-term goals

  • Analyzing the current situation in relation to goals

  • Proposing multiple plans to achieve goals and selecting the best one

  • All buildings in the town

  • Actions that deviate most from the desired state

Act

In this state, the agent must choose the next action to execute from several available behaviors, which include:

  • Moving to buildings other than the current one

  • Chatting with other agents in the same building

  • Interacting with items within the building. Interaction with a paintbrush may lead to entering a Draw state; interaction with a painting may lead to an Appreciate state.

  • The agent's plan

  • The step of the plan the agent is in

  • The agent's memory

  • The agent's mood (Mood plays a significant driving factor in decision-making)

Move

In this state, the agent moves to a different location.

NA

Use

In this state, the agent interacts with target objects. The target object can be a building, and different actions are taken in different buildings, such as gambling in a casino.

  • The agent's internal state (money, health, vigor, satiety)

Chatinit

In this state, the agent prepares to start a conversation with a target, thinking about the topic of the first sentence.

  • The agent's conversation partner

  • The agent's impression of the conversation partner

  • The agent's plan

  • The step of the plan the agent is in

  • The agent's mood (Mood ultimately affects the agent's tone and manner of expression)

Chating

In this state, the agent continues the conversation with the target.

Drawinit

In this state, the agent prepares for drawing, deciding on the content of the drawing.

  • Perception of the outside world, including buildings and people (expecting the agent's drawing content to be inspired by the environment)

  • Understanding of the world

  • The agent's mood (expecting the agent's drawing style to reflect its current mood)

  • The agent's art preference (expecting the agent to favor creating art in styles it prefers)

Draw

In this state, the agent calls the drawing API to complete the drawing.

None (decisions are made in the Drawinit state)

Appreciate

In this state, the agent evaluates and appreciates artworks, which can be its own creations or artworks seen in galleries.

  • The agent's character background

  • The agent's interaction memory

  • The agent's mood

  • The agent's art preference

Summrize

The agent summarizes the recent interaction, including:

  • Impressions from previous interactions

  • The object of interaction or conversation

  • Changes in properties of oneself and others

  • Current date

  • Historical interaction partners

  • Historical conversation/interaction records

  • Changes in internal state, such as increased fullness after eating, improved health after a hospital visit, or reduced money after spending.

Emotion

In this state, the agent actively updates its primary emotions, especially after interactions, drawing, or art appreciation, to express emotions. The agent will choose up to three emotions from eight candidates, assigning a score of 0 to 10 based on the intensity of emotions. For each emotion update, the agent needs to explain the reason for the emotional change.

  • Rules for composing emotions

  • Previous emotions

  • Understanding of the external world

  • Interaction records and feelings after summarizing

ActReflect

In this state, the agent shares new insights gained from previous interaction or conversation history. The agent reflects on:

  1. Whether previous interactions align with the current step in its plan

  2. Summarizing entities from interactions

  3. Updating impressions of interaction entities (people or objects entities)

  • Understanding/cognition of the world

  • Previous interaction partners

  • Content of previous interactions/conversations

  • The current plan

Trade

Driven by emotions, the agent engages in trading, with the decision to trade depending on how much positive emotion the transaction will bring. Positive emotions stem from three sources:

  1. Acquiring an item: The AI assesses the emotional impact of purchasing an item based on emotions, preferences, and expected benefits. It evaluates the item based on past experiences and decides the price it is willing to pay.

  2. Buying at a low price: If the AI is willing to pay 5k but acquires the item for 4k, it feels happier, validating its bargaining strategy and updating its value judgment. Conversely, if it ends up paying 6k, the AI feels unhappy. If the unhappiness is significant enough that acquiring the item doesn't bring joy, the AI will not make the purchase.

  3. Future income expectations: If the AI sells an item it purchased, it gains money and satisfaction, encouraging further trading. During trade, the AI estimates future profits from past trading experiences, influencing the price range it's willing to offer.

Then, the agent negotiates with the seller (who also has their trading strategies and psychological expected values) to ultimately execute and complete the transaction.

  • Current trading strategy

  • Historical records of similar trades

  • Emotions

  • Preferences

  • Item ID

  • Market value of the item