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Multi-Agent Simulator

Experiment with agent systems and visualize emergent behaviors

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Designing Agent Behaviors

An agent's behavior determines how it perceives the world, makes decisions, and takes action. Different behavior architectures suit different tasks: reactive agents excel at real-time response, proactive agents handle complex planning, and hybrid agents balance both.

Three Core Behavior Types

⚡

Reactive

Responds immediately to environment stimuli

Fast decision-makingNo internal stateStimulus-response mapping
🎯

Proactive

Plans actions to achieve goals

Goal-orientedFuture planningInternal world model
🔄

Hybrid

Combines reactive and deliberative layers

Quick reflexesStrategic planningBest of both worlds

Interactive: Behavior Designer

Select a behavior type and tune its capabilities. Watch how different configurations affect agent performance.

BEHAVIOR ARCHITECTURE

70%
How accurately the agent senses its environment
50%
Complexity of decision-making and planning
30%
How quickly the agent adapts from experience
Overall Performance Score
54
out of 100
Decision Speed
Very Fast
Adaptability
Low
Recommended For
Real-time Control

Design Considerations

⚡ Reactive Best For:
  • • Robot obstacle avoidance
  • • Real-time game NPCs
  • • Emergency response systems
🎯 Proactive Best For:
  • • Strategic game AI
  • • Resource allocation
  • • Long-term optimization
🔄 Hybrid Best For:
  • • Autonomous vehicles
  • • Adaptive assistants
  • • Complex simulations
📊 Performance Trade-offs:
  • • Speed vs. planning depth
  • • Memory vs. simplicity
  • • Adaptability vs. stability

💡 Key Insight

There's no universally "best" behavior architecture. The right choice depends on your task constraints: reactive agents win when milliseconds matter, proactive agents excel at complex goal achievement, and hybrid architectures handle real-world complexity. Design your agents for their specific environment and objectives.

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