Emote-AI 2.1

A Minimal Framework for Agent Behavior Using Desire, Anxiety, and Confidence

Author: Cory R. Carlson Repo: github.com/pysocrates/emote-ai

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Concept Update: Emote-AI 2.1

In the original Emote-AI framework, agent behavior was governed by just two emotional primitives:

• Desire: Goal-oriented motivation (the "pull" toward outcomes)
• Anxiety: Regulatory tension due to uncertainty, failure risk, or external obstacles

The Emote-AI 2.x series adds a third primitive to balance the system:

• Confidence (introduced in the 2.x series): The agent's internal assessment of its ability to succeed based on past outcomes and current situational clarity

Version 2.1 focuses on environmental safety in the grid-world demo:

• Hazard-driven anxiety behavior raises the anxiety scalar when hazards (or fog-obscured risks) approach, nudging the agent toward safer, slower routes.
• No-freeze safeguard prevents the agent from getting stuck at hazard walls by invoking A* route-seeking before movement stalls, ensuring progress without stepping into danger.

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Core Primitives

1. Desire

• Range: 0.0 (disinterest) to 1.0 (compulsion)
• Drives goal pursuit
• Increases when tasks are valuable or pressing

2. Anxiety

• Range: 0.0 (calm) to 1.0 (panic)
• Rises with uncertainty, obstacles, or repeated failure
• Modulates risk-awareness and caution

3. Confidence (introduced in the 2.x series)

• Range: 0.0 (no self-efficacy) to 1.0 (total certainty)
• Increases with successful task execution
• Decreases with failure, interruption, or insufficient feedback
• Works with the 2.1 hazard response to relax defensive routing once threats dissipate

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Behavioral Logic Grid

Desire	Anxiety	Confidence	Resulting Behavior
High	Low	High	Direct, assertive pursuit of goal
High	High	Low	Cautious action, fallback planning, info-seeking
Low	Low	Moderate	Idle, or minimal energy task-switching
High	Low	Low	Reckless pursuit, may trigger failure loop
Moderate	Moderate	High	Maintain course, open to adaptation

Agents should adjust their behavioral plan dynamically based on shifts in these three state values.

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Implementation Sketch

class EmotionLiteAgent:
    def __init__(self):
        self.desire = 0.6
        self.anxiety = 0.2
        self.confidence = 0.7

    def assess_context(self, success, obstacle, feedback):
        # Simulate learning and adaptation
        if success:
            self.confidence = min(1.0, self.confidence + 0.1)
            self.anxiety = max(0.0, self.anxiety - 0.05)
        if obstacle:
            self.anxiety = min(1.0, self.anxiety + 0.1)
            self.confidence = max(0.0, self.confidence - 0.05)
        if not feedback:
            self.confidence = max(0.0, self.confidence - 0.05)

    def choose_behavior(self):
        if self.desire > 0.7 and self.anxiety < 0.4 and self.confidence > 0.6:
            return "Pursue goal directly"
        elif self.anxiety > 0.6 and self.confidence < 0.4:
            return "Fallback plan or request help"
        elif self.desire < 0.3:
            return "Idle or passive state"
        else:
            return "Cautious pursuit with monitoring"

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Design Goals

• Maintain interpretable agent behavior
• Avoid full emotion simulation while allowing emotional realism
• Enable safe and predictable adaptation to uncertainty and feedback

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Future Directions

• Incorporate memory of prior outcomes to influence confidence decay
• Add optional fourth scalar: Curiosity or Patience
• Explore agent "mood drift" over time from uneven balance of primitives
• Apply to RL environments with real reward shaping and goal prioritization

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Summary

Emote-AI 2.1 builds on the original minimal framework by pairing the confidence scalar with hazard-aware anxiety modulation and the no-freeze safeguard, giving agents the ability to self-regulate risk while staying mobile. The goal remains clarity, safety, and low-complexity control over behavioral logic for intelligent agents in alignment-critical contexts.