Category: Phase 2 – NLP beginning

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.

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.