Artificial Computational Intelligence

Introduction

25-July

Prerequisites

1. Basic probability & statistics.
2. Propositional/Boolean logic, laws, and rules.
3. Fundamental algorithms and data structure concepts.
4. Coding proficiency in Python.

Scope : Covers fondation for agentic AI.

Module 1’s agenda – PEAS Framework: The PEAS framework (Performance Measure, Environment, Actuators, Sensors) .

Crux: How to represent knowledge using probabilistic way.

Index

• Terminology

Introduction to Core AI Terminology (via Navigation Example)

AI terminology using a self-autonomous car / traveling agent toy example:

1. Agent / Intelligent Agent: An entity capable of perceiving its environment and taking actions that mimic human reasoning or behavior.
2. Environment: The external world or context in which the agent operates (e.g., the map/locality).
3. Exploration: The process by which an agent traverses unknown paths when no prior map data is available.
4. Exploitation: Using the shortest known route already stored in its Knowledge Base to travel from the Initial State to the Goal State as quickly as possible.
5. Fact – Learnings from exploration.
6. Knowledge Base (KB): The agent’s memory where explored routes, map layouts, and learned facts are stored for future decision-making.
7. Sensors & Percepts:
   • Sensors: Hardware/software tools (cameras, LIDAR, etc.) used to observe the environment.
   • Percept: The specific input or sensory observation received from the environment at any given time.
8. Actuators & Actions:
   • Actuators: Mechanisms enabling the agent to interact with the environment.
   • Actions: Decisions executed by the agent (e.g., move forward, turn left/right, apply brakes).
9. Initial State & Transition Model:
   • Initial State: The starting point/condition of the problem (e.g., the Source location).
   • Transition Model: The logical framework defining how taking a specific action from a given state shifts the agent into a new resultant state (e.g., moving right from Source leads to Junction 1).

The Core Problem: Why Balance is Necessary

• Pure Exploitation: If an agent only exploits what it knows early on, it acts greedily. It will get stuck in a local optimum because it never attempts actions that could lead to much higher rewards overall.
• Pure Exploration: If an agent spends all its time exploring, it wastes compute power, time, and resources randomly sampling paths without ever taking advantage of the high-reward paths it already found.

Reference:

⁠Artificial Intelligence: A Modern Approach (4th Edition) by Russell & Norvig.

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1-Aug-2026

Index

1. Inteliggence
2. 4 perspective of AI
3. State space representation
4. Environment

What is Intelligence?

• Definition: The capacity to perceive environmental data, reason from available facts, plan/take decisions, and learn/adapt to patterns.
• Key Observations:
  • Are humans always intelligent? No—humans make mistakes and get emotional.
  • Can animals be intelligent? Yes (e.g., dogs tracking scent, dolphins communicating, cats sensing earthquakes early).
  • Can machines be intelligent? Yes (e.g., Chess AI, Medical AI, Autonomous driving).
  • Takeaway: Intelligence is not exclusive to human beings.
  • Human vs. AI Learning: Humans learn natively by observing and imitating; AI systems rely on structured data feeding (50%–80%+ initial training datasets) before adapting.

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The Four Perspectives of AI (Russell & Norvig Framework)

Perspective	Core Concept	Lecture Explanation & Example
1. Thinking Humanly	Cognitive Modeling	Replicating the human thought process (using brain scans, psychology, and cognitive science).
2. Acting Humanly	Behavioral Approach (Turing Test)	Focusing purely on external actions and outcomes that behave like a human, regardless of internal thought mechanisms.
3. Thinking Rationally	Laws of Thought / Logic	Deriving logically correct conclusions from formal premises using logic.
4. Acting Rationally	Rational Agent	Selecting the best possible action expected to maximize goals / performance measures.

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State-Space Representation :

In Data Structures & Algorithms (DSA) and System Design interviews, candidates are often evaluated on how compactly they represent system states.

As demonstrated with the Vacuum Cleaner World (Location, Status_A, Status_B), storing raw image frames or bloated objects causes state-space explosion and memory overhead.

Real-World Application

• Game AI & Simulators: Games like Chess or Go use compact bitboards (bitmasks) to represent pieces on a board rather than complex 2D arrays, speeding up state evaluation by orders of magnitude.
• Navigation & Routing (Google Maps, Uber): Road networks are compressed into state graphs where states are intersections and edges are road segments with dynamic weight (traffic latency).

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Task Environment Classification (PEAS Framework)

• PEAS Profile for Automated Taxi Driver:
  • P (Performance Measure): Safe, fast, legal, comfortable trip; profit maximization.
  • E (Environment): Roads, traffic, pedestrians, weather.
  • A (Actuators): Steering, accelerator, brake, signal, horn.
  • S (Sensors): Cameras, LIDAR, speedometer, GPS.

Environment Properties:

1. Fully Observable vs. Partially Observable: Whether sensory input provides complete state visibility or limited/restricted visibility (e.g., hidden rooms or obstructed views).
2. Deterministic vs. Stochastic: Whether executing action A in state S always yields the exact same outcome $S’$, or introduces uncertainty.
3. Episodic vs. Sequential: Whether current actions affect future decisions (sequential) or if each perceptual episode is independent (episodic).
4. Static vs. Dynamic / Discrete vs. Continuous