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Knowledge-Based Systems & Multi-Agent Environments - Coggle Diagram
Knowledge-Based Systems & Multi-Agent Environments
Logical Agents
Go beyond reflexes – use reasoning
Rely on a Knowledge Base (KB)
TELL: Add perceptual info
ASK: Query for next action
Actions based on inference from known rules (axioms)
Knowledge Representation
Declarative: Facts/rules added to KB
Procedural: Logic encoded in code
Knowledge level vs implementation level
(e.g., taxi “knows” route regardless of how map is stored)
Wumpus World Example
Grid-based environment (4x4)
Elements: Wumpus, gold, pits, agent
Sensors:
Stench (Wumpus nearby)
Breeze (Pit nearby)
Glitter (Gold nearby)
Bump (Wall)
Scream (Wumpus killed)
P.E.A.S. for Wumpus World:
Performance: +1000 (escape w/ gold), -1000 (death), -1/action, -10/use arrow
Environment: Static, discrete, partially observable
Actuators: Move, turn, grab, shoot, climb
Sensors: Percepts list
Multi-Agent Environments
Agents plan independently, coordinate to reach shared goal
Coordination strategies
Conventions: Stay in your zone (e.g., tennis)
Communication: “It’s my ball!”
Plan recognition
Biological Inspiration
Evolution of conventions
Examples:
Ants, bees, bats, fish swarms
Salmon migration, zebra stripes
Craig Reynolds’ Boids Model
Flocking behaviors:
Separation – avoid crowding
Alignment – match direction
Cohesion – move toward group
Conway’s Game of Life
Simple cellular automata with emergence
Rules:
Live cell dies (loneliness or overcrowding)
Dead cell becomes live (3 neighbors)
Leads to:
Still lifes (block, boat)
Oscillators (blinker, toad)
Gliders (movement, signal transmission)
Artificial Life (ALIFE)
Models biological behaviors (evolution, swarming, synthetic biology)
Used in:
Art & music
Simulation of diseases
Generative systems
Collective Intelligence
• Crowdsourcing (e.g., Amazon Mechanical Turk)
• Decision-making through group behavior
• Neural networks as natural, adaptive intelligence
• Used in pattern detection and decentralized systems