Goal based agents-path finding and Reflex and goal-based agents-making decisions
Path finding
Problem solving
State space
Node and Edges
Graph
Types of search
Well defined problem
Finding the shortest path between two points in a maze
Weighted edge
Digraph
DFS- Depth First Search
BFS- Breadth First Search
Goal based agent making decision
Goal-based agents' function in the decision-making process
The capacity of ML and AI to enhance the decision-making abilities of goal-based agents
Analyzing the role of emotions and affective states in the decision-making process of goal-based agents.
Examining the impact of feedback loops on the decision-making process of goal-based agents.
Evaluating the ethical implications of using goal-based agents in decision-making processes, particularly in high-stakes situations.
How goal-based agents might change their choices in response to altering circumstances or new information
Agent
What is agent ?
Percept
Rational Agent
Rationality
Task Environment
PEAS
Learning Agent
Agents' need for perceptual learning and adaptation
How agents navigate their environment using perceptual information
various perceptual sensors employed by agents
How perception affects how an agent makes decisions
Agent Type
Simple Reflex
Model - Based Reflex
Utility - Based
Goal Based
Understanding the concept of Simple Reflex in Agent Type
How Simple Reflex is used in Goal-based agents for path finding
The role of Simple Reflex in decision making for Reflex and goal-based agents
State machines
Deterministic
Non - Deterministic
Finite state machine (FSM)
State defines behaviour and produces action
Transitions between states
Rules and conditions that lead to transition
Input events = triggers
Modelling a system
state Transition machine
Embed in Classes
Relatively simple to code
Easy to debug - each state separate object
Follows hard-coded rules so less processing power (less intelligence)
Intuitive to model
Flexible - can tweak behaviour
Can represent its own history
Best First path search
*A Search