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Knowledge based systems and Pathfindng algorithms - Coggle Diagram
Knowledge based systems and Pathfindng algorithms
Logical agents
Use internal knowledge representations to guide reasoning.
Pathfinding agents are restricted.
Human responses go beyond reflexes.
Require a knowledge base
Knowledge base
Artificial intelligence knowledge bases seek to incorporate human expert knowledge to facilitate decision-making, problem-solving, and other processes. Knowledge base systems have been created over time to assist numerous organizational procedures.
Knowledge base agents
Agents with the ability to retain an internal state of information, reason about that knowledge, update their knowledge based on observations, and take action are known as knowledge base agents.
Must be able to adapt to new perceptions.
The capacity to adjust one's internal world representation.
State and action representation should be possible for agents.
Adopts the proper measures.
Wumpus world
A straightforward example that highlights the value of a knowledge-based agent and serves to symbolize knowledge representation is the wumpus world. It was influenced by Gregory Yob's 1973 video game Hunt the Wumpus.
It is sequential because the order is significant.
Pits and Wumpus are not moving, hence it is static.
Deterministic in nature, since the world's fate is predetermined.
There is a distinct environment.
Because the agent can only see things close by, such a room next door, the Wumpus world is only partially viewable.
Since Wumpus is not regarded as an agent and we only have one, the environment is one agent.
Web links
https://www.javatpoint.com/the-wumpus-world-in-artificial-intelligence
https://www.javatpoint.com/knowledge-based-agent-in-ai
https://www.indeed.com/career-advice/career-development/what-is-knowledge-based-system
https://courses.edx.org/asset-v1:ColumbiaX+CSMM.101x+1T2017+type@asset+block@AI_edx_logic__2_.pdf
https://www.almabetter.com/bytes/tutorials/artificial-intelligence/state-space-search-in-artificial-intelligence
https://www.javatpoint.com/binary-search-tree
https://www.javatpoint.com/breadth-first-search-algorithm
https://www.javatpoint.com/ai-uninformed-search-algorithms
State space
Collection of every state that is accessible from the beginning.
Search tree
A data structure used to show data in a hierarchical format.
One type of data structure used to represent data in a hierarchical format is a tree. IT is characterized as a group of individuals or objects, referred to as nodes, that are connected to one another to create the illusion of a hierarchy.
There are nodes and branches in this strategy.
Every node represents a state, and every branch denotes an activity.
The frontier
The location where the target node is reached. It is referred to as a set of nodes in other words. Parent nodes, child nodes, and leaf nodes make up a tree search.
Child node - current state
Leaf node - Ends of path on frontier
Parent node - previous state
Measuring performance
Optimality - lowest path cost
Time complexity - how long it takes
Completeness - guarantee find solution
Space complexity - how much memory required
Searching algorithms
Uninformed search
Other than the information given in the problem statement, these algorithms contain no extra information about the objective node.
Depth first
LIFO
Backtracking search uses less memory
Depth limited
Has a predetermined limit (no infinity)
What is the diameter of the problem?
Uniform cost
Expands node with lowest path cost
Changes order of QUEUE
Iterative deepening depth first
This limit gradually increases
Breadth first
FIFO - first in first out of the queue
Memory bigger problem than execution time
Bidirectional
Informed search
These algorithms aid in more effective searching since they contain information about the objective state.
Greedy Best First Search
Node grows in proportion to the evaluation function f(n).
This is the basis for both A* and Greedy Best First Search.
More effective in contrast to BFS.
A*
Cost of each node = cost of edge + cost of previous nodes + estimated cost to reach target
It is perfect and comprehensive.
f(n) = g(n) + h(n)
Take into account an estimate of cost to target.
Underestimate guarantees optimal path.
Stops you heading in wrong direction.
Best first Search
Enlarges the node nearest the objective.
Merely applying the heuristic. Consequently, f(n) = h(n).
Doesn't investigate the additional nodes that make up the algorithm again.
Dijkstra’s Algorithm
Node selection and cost assessment.
Priority line and unwinding.
Algorithm with the shortest path.
Genetic algorithms
Randomly match 2 parents.
Swap match (like DNA) for offspring.
Determine probability of selection.
Random mutation.
Measure fitness of each possibility.
Slime mould
A simple organism that consists of an acellular mass of creeping jelly-like protoplasm containing nuclei, or a mass of amoeboid cells. When it reaches a certain size it forms a large number of spore cases.
