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Knowledge-Based Systems and Multi-Agent Environments - Coggle Diagram
Knowledge-Based Systems and Multi-Agent Environments
Logical Agents
Reason based on internal knowledge
Use TELL (add) and ASK (query)
Need a Knowledge Base
Knowledge Base Concepts
Declarative vs Procedural approaches
Knowledge level vs Implementation level
Sentences = assertions (facts or axioms)
Wumpus World Example
Environment: 4x4 grid
Agents:
o Sensors (smell, breeze, glitter, bump, scream) o o Actuators (move, grab, shoot, climb)
PEAS Model : o Performance: +1000 (gold), -1000 (pit/wumpus), penalties for actions o Environment: Partially observable, static, discrete
Multi-Agent Environments
Need planning, cooperation, communication
Methods: o Convention (follow unwritten rules) o Communication (explicit signaling) o Plan Recognition (predict partner moves)
Emergent Behaviours
Flocking and Swarming: o Separation, Alignment, Cohesion, o Craig Reynolds' Boids simulation
Biological inspiration: bees, ants, bats
Cellular Automata & Conway’s Game of Life
Simple rules → Complex patterns
Examples: Still life, oscillators, gliders
Concept: Study of emergence from simple behaviors
Artificial Life (ALIFE)
Simulate evolution, behavior, ecosystems
Alternative genetics and synthetic biology
Applications: generative music, modelling life-like systems
Crowd Sourcing and Collective Intelligence
Example: Amazon Mechanical Turk
Neural networks and natural collective intelligence
Crowd collaboration to solve tasks