AI & ML Concepts
Explore the key artificial intelligence and machine learning concepts demonstrated in our interactive simulation.
Reinforcement Learning
Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.
Key Points:
- •Agents learn through trial and error
- •Actions lead to rewards or penalties
- •The goal is to maximize long-term rewards
- •Q-learning and policy gradients are common techniques
In Our Simulation:
In our simulation, civilizations learn optimal resource management and diplomatic strategies through rewards from successful growth and penalties from conflicts.
Behavioral Trees
Behavioral trees are a mathematical model of plan execution used in computer science, robotics, and artificial intelligence. They describe switchings between a finite set of tasks in a modular fashion.
Key Points:
- •Hierarchical structure for decision-making
- •Composed of selector, sequence, condition, and action nodes
- •Enables complex, modular behaviors
- •Provides visual representation of AI logic
In Our Simulation:
Civilizations use behavioral trees to make diplomatic and economic decisions, evaluating conditions like resource levels and trust before taking actions.
Evolutionary Algorithms
Evolutionary algorithms are optimization algorithms inspired by biological evolution, using mechanisms like mutation, crossover, and selection to find optimal solutions to problems.
Key Points:
- •Population of potential solutions
- •Fitness function evaluates solutions
- •Selection favors better solutions
- •Mutation and crossover create new solutions
In Our Simulation:
Technologies and cultural traits in civilizations evolve over time, with successful strategies being selected and refined through generations.
Neural Networks
Neural networks are computing systems inspired by the biological neural networks in animal brains. They can learn to perform tasks by considering examples, without being explicitly programmed for the task.
Key Points:
- •Interconnected nodes (neurons) process information
- •Weighted connections determine information flow
- •Learning occurs by adjusting connection weights
- •Deep learning uses multiple layers of neurons
In Our Simulation:
Civilizations use neural networks to predict outcomes of conflicts, evaluate diplomatic relations, and optimize resource allocation strategies.
Multi-Agent Systems
Multi-agent systems are computational systems where multiple intelligent agents interact with each other. The agents are autonomous entities that observe and act upon an environment.
Key Points:
- •Multiple AI entities in a shared environment
- •Agents have individual goals and knowledge
- •Emergent behaviors arise from interactions
- •Can involve cooperation or competition
In Our Simulation:
The two civilizations in our simulation are independent agents that interact through trade, diplomacy, and sometimes conflict, creating complex emergent behaviors.
Graph Neural Networks
Graph Neural Networks (GNNs) are neural networks that can directly operate on graph structures. They're designed to process data that can be represented as graphs with nodes and edges.
Key Points:
- •Process data structured as graphs
- •Learn from node features and connections
- •Capture relationships between entities
- •Useful for network and relationship data
In Our Simulation:
Trade networks and diplomatic relationships between cities are modeled as graphs, with GNNs helping to predict how these relationships will evolve.
Genetic Programming
Genetic Programming is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user-defined task.
Key Points:
- •Evolves computer programs or algorithms
- •Programs represented as tree structures
- •Crossover exchanges subtrees between programs
- •Mutation randomly changes parts of programs
In Our Simulation:
Civilizations develop unique approaches to resource allocation and city planning through genetic programming, evolving their strategies over time.
Decision Theory
Decision theory is concerned with identifying the values, uncertainties, and other issues relevant in a given decision and determining the optimal decision.
Key Points:
- •Mathematical framework for making optimal decisions
- •Considers probabilities of outcomes
- •Evaluates utility of different outcomes
- •Balances exploration vs. exploitation
In Our Simulation:
AI leaders use decision theory to evaluate the risks and benefits of actions like declaring war, forming alliances, or investing in different technologies.
See These Concepts in Action
Our interactive simulation demonstrates these AI and ML concepts through the evolution and interaction of two civilizations.
Launch Simulation