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Questions and Answers
What is the purpose of defining a state space in problem solving?
What is the purpose of defining a state space in problem solving?
- To identify the operators available for actions.
- To outline all possible configurations in a problem. (correct)
- To restrict the number of states to be considered.
- To analyze the goal state and initial situation.
Which of the following is NOT one of the uninformed search strategies mentioned?
Which of the following is NOT one of the uninformed search strategies mentioned?
- Bidirectional search
- Depth-first search
- Simulated annealing (correct)
- Iterative deepening
What key component is defined by identifying starting points and successful outcomes in state space search?
What key component is defined by identifying starting points and successful outcomes in state space search?
- Operator set
- State configuration
- Search path
- Initial state and goal state (correct)
In the context of the 8-puzzle game, which statement best describes the term 'problem state'?
In the context of the 8-puzzle game, which statement best describes the term 'problem state'?
What is the significance of operators in state space search?
What is the significance of operators in state space search?
Which search strategy explores all neighbors at the present depth before moving on?
Which search strategy explores all neighbors at the present depth before moving on?
Which of the following best describes uniform-cost search?
Which of the following best describes uniform-cost search?
In the problem solving domain, what does the term 'goal state' refer to?
In the problem solving domain, what does the term 'goal state' refer to?
What is the primary procedure followed in AI production systems?
What is the primary procedure followed in AI production systems?
What defines the control strategy in AI production systems?
What defines the control strategy in AI production systems?
How can the computational cost of an AI production system be categorized?
How can the computational cost of an AI production system be categorized?
What happens at the uninformed extreme of the control strategy?
What happens at the uninformed extreme of the control strategy?
At the informed extreme of rule selection, what is the strategy primarily based upon?
At the informed extreme of rule selection, what is the strategy primarily based upon?
What is a consequence of a high rule application cost in an uninformed control system?
What is a consequence of a high rule application cost in an uninformed control system?
What is considered the termination condition in an AI production system?
What is considered the termination condition in an AI production system?
What constitutes a search process in operations of AI production systems?
What constitutes a search process in operations of AI production systems?
What constitutes the control strategy in a production system?
What constitutes the control strategy in a production system?
Which of the following statements best describes a production system?
Which of the following statements best describes a production system?
What is the role of eligible rules in a production system?
What is the role of eligible rules in a production system?
Which of the following statements accurately conveys a difference between production systems and conventional computation?
Which of the following statements accurately conveys a difference between production systems and conventional computation?
What issue does the representation problem in AI address?
What issue does the representation problem in AI address?
What happens when a rule in a production system is fired?
What happens when a rule in a production system is fired?
How does a control strategy impact the operation of a production system?
How does a control strategy impact the operation of a production system?
Why can the modular structure of production systems be advantageous?
Why can the modular structure of production systems be advantageous?
What does the variable 'd' represent in the context of state space search?
What does the variable 'd' represent in the context of state space search?
If a node has a branching factor of 2 and the goal is at depth 3, how many nodes will be expanded at depth 2?
If a node has a branching factor of 2 and the goal is at depth 3, how many nodes will be expanded at depth 2?
Which list is used to keep track of nodes that have already been expanded in a graph search procedure?
Which list is used to keep track of nodes that have already been expanded in a graph search procedure?
What happens if the open list becomes empty during a graph search?
What happens if the open list becomes empty during a graph search?
In a depth-first search, where are new nodes added to the open list?
In a depth-first search, where are new nodes added to the open list?
What is the purpose of checking if the selected node is the goal node in the graph search?
What is the purpose of checking if the selected node is the goal node in the graph search?
Which strategy is indicated by adding nodes to the end of the open list?
Which strategy is indicated by adding nodes to the end of the open list?
If the maximum depth of the search state space is 'm', what does this imply?
If the maximum depth of the search state space is 'm', what does this imply?
What is the order of nodes generated in breadth-first search at depth $d$ with a branching factor $b$?
What is the order of nodes generated in breadth-first search at depth $d$ with a branching factor $b$?
What space complexity does breadth-first search have due to its nature of expanding nodes?
What space complexity does breadth-first search have due to its nature of expanding nodes?
When is breadth-first search considered optimal?
When is breadth-first search considered optimal?
In depth-first search, how are successors of a node placed in the queue?
In depth-first search, how are successors of a node placed in the queue?
What does depth-first search prioritize during its node expansion?
What does depth-first search prioritize during its node expansion?
If a node has successors B and C, how are they handled in depth-first search?
If a node has successors B and C, how are they handled in depth-first search?
Which search method guarantees that all states will be examined if the branching factor is finite?
Which search method guarantees that all states will be examined if the branching factor is finite?
When checking if a node is a goal state in depth-first search, what is the first step?
When checking if a node is a goal state in depth-first search, what is the first step?
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Study Notes
AI Production Systems
- An AI production system operates through a search process. Rules are tried until a sequence of them satisfies the termination condition.
- A production system is composed of:
- Global Database: Contains facts and information.
- Rules: Production rules that express knowledge.
- Control Strategy: Defines how rules are selected and executed.
- Representation Problem: Transforming a problem statement into these three components.
- Working Memory: The database in a production system.
- Rule Eligibility: A rule becomes eligible to fire when its conditions match elements in the working memory.
- Rule Firing: Applying a rule to the working memory generates new facts.
- Control Strategy Determines: Which eligible rule to fire next.
Production Systems vs. Conventional Computation
- All rules have access to the global database, unlike conventional systems where sections of the program may have limited access.
- Rules in a production system don't directly call other rules. Communication happens through the global database.
- Production systems are modular, allowing independent changes to components.
- Conventional computation requires extensive changes if the knowledge space changes, while production systems are more adaptable.
Procedural Steps in a Production System
- Start with an initial database and apply rules until the termination condition is met.
- Select a rule from the rule set that can be applied to the current data.
- Apply the rule to generate new data.
- Repeat this process until the termination condition is satisfied, reaching the goal state.
Control Strategies
- Control strategy: Selecting rules and tracking the sequences of rules already tried.
- Uninformed Control: When little is known about the problem, rule selection can be arbitrary.
- Informed Control: When knowledge about the problem guides the control strategy to select the right rule.
Computational Cost
- Rule application cost is high for uninformed control systems as numerous rules need to be tried.
- Uninformed Search: An initial step in understanding search in AI.
Problem Solving as Search
- State space: Contains all possible configurations of the problem.
- Initial state: The starting point of the problem.
- Goal state: The desired solution to the problem.
- Operators: Rules describing actions or operations available.
Graph Search Procedure
- Start Node (s): The initial state of the problem.
- Successors: Nodes reachable from the current node.
- Open List: Contains nodes waiting to be explored.
- Closed List: Contains nodes that have already been explored.
- Expansion: Generating successors of a node.
- Goal Check: Determining if the current node is the goal node.
- Failure: Exiting the search if there are no more nodes to expand (empty open list).
Breadth-First Search
- Expands nodes level by level, starting from the initial state.
- Open List Implementation: Queue with first-in, first-out (FIFO) behavior.
- Completeness: For finite branching factor (b), all nodes will be explored eventually.
- Time Complexity: O(b^d) - exponential growth based on branching factor (b) and depth (d) of the shallowest goal node.
- Space Complexity: O(b^d) - stores all nodes examined, leading to exponential space requirement.
- Optimality: Guaranteed for unit step cost (every action has a cost of 1), as it will find the shallowest goal first.
Depth-First Search
- Expands the deepest unexpanded node.
- Open List Implementation: Last-in, first-out (LIFO) behavior, similar to a stack.
- Completeness: May not be complete for infinite depth or cycles.
- Time Complexity: O(b^m) - exponential, based on branching factor (b) and maximum depth (m) of the search space.
- Space Complexity: O(bm) - stores only the path to the currently explored node.
- Optimality: Not guaranteed, might find a deeper solution before a shallower, more optimal one.
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