Questions and Answers
What is a Problem-Solving Agent?
What is the computational process undertaken by problem-solving agents called?
search
Which of the following represents states in a problem-solving context?
In a state space graph, each state occurs multiple times.
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What is the goal when traveling from Arad to Bucharest?
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The process of removing detail from a representation is called ______.
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What is represented by the arcs in a state space graph?
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What is a search tree?
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What condition must each node in a graph satisfy for Euler's solution to exist?
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What is a knowledge base in the context of a knowledge-based agent?
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What does the agent do after receiving a percept?
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In the Wumpus World, what is the performance measure for the agent?
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The agent can always guarantee to get the gold in Wumpus World.
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What are the actuators available to the agent in Wumpus World?
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What does the agent sense when it moves adjacent to a wumpus?
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In propositional logic, if a sentence $\alpha$ is true in model $m$, then $m$ ___ $\alpha$.
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Models are non-ambiguous formal languages.
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What does it mean for knowledge base KB to entail a sentence $\alpha$?
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What are the sensors available to the agent in Wumpus World?
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Study Notes
Agents to Solve Problems
- Problem-solving agents operate based on goals, choosing actions that lead to desirable states.
- The search process is essential for problem-solving, allowing agents to identify efficient action sequences.
- Atomic representations deem states as wholes, while planning agents utilize structured representations.
Problem Formulation
- A search problem encompasses several components:
- State Space: Set of all possible states in the environment.
- Initial State: Starting point for the agent.
- Goal Test: Condition determining if the goal has been achieved.
- Successor Function: Outlines state changes due to actions, including the costs associated with these actions.
- Solutions present as sequences of actions, effectively transforming the initial state to the goal state.
Travel Example
- In a hypothetical scenario, traveling in Romania from Arad to Bucharest:
- Goal: Reach Bucharest.
- States: Inhabit different cities.
- Actions: Drive to connected cities with defined costs.
Abstraction
- Problem formulation provides a model that abstracts the real situation by ignoring extraneous details.
- The concept is exemplified by Euler's Seven Bridges of Königsberg, which proved through graph theory that traversing all bridges once is impossible unless each node has an even degree.
Route Finding Problem
- Route finding exemplifies how to structure a search problem using cities and actions:
- States: Various cities en route.
- Actions: Travel to an adjacent city.
- Goal Test: Check if the destination is Bucharest.
- Path Cost: Measured by distance traveled.
State Space Graphs and Search Trees
- State Space Graph: Represents search problems mathematically, with nodes symbolizing configurations and arcs indicating action results. Only unique states are present.
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Search Tree: A visualization tool depicting possible plans and outcomes:
- Root node represents the initial state.
- Children nodes correspond to the successors of actions.
- Each node illustrates plans leading to states but represents paths in the state space as a whole.
Key Differences
- In a State Space Graph, each configuration is unique and represented once, aiding in understanding potential outcomes without memory saturation.
- A Search Tree illustrates plans and their consequences but cannot fully encapsulate every possible outcome due to size constraints. Each node corresponds to a path, reflecting potential progress toward goals.
Logical Agents
- A knowledge-based agent uses a knowledge base (KB) made up of formal language sentences.
- The KB-Agent processes inputs through a series of steps to make decisions and perform actions.
Wumpus World
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Defined using the PEAS framework:
- Performance Measure: Gold (+1000), Death (-1000), Each Move (-1), Arrow Use (-10).
- Environment: 4x4 grid with randomly placed gold and wumpus; pits have a 20% probability.
- Agent starts at position [1, 1] facing right, with specific actuators and sensors.
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Actuators include:
- Forward, TurnLeft, TurnRight, Grab, Shoot (limited use), Climb (only at [1, 1]).
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Sensors detect environmental conditions:
- Stench (near wumpus), Breeze (near pit), Glitter (gold), Bump (wall), Scream (when wumpus dies).
Decision Challenges
- Agents must weigh the risk of retrieving gold against potential hazards in the environment.
- A Breeze percept at [1, 1] does not clarify which adjacent directions are safe due to uncertainty.
- Initial knowledge includes the rules of the environment, understanding it starts at a safe square.
Steps of the Agent's Actions
- The agent perceives environmental conditions and updates its knowledge.
- Moves between squares based on its percepts (e.g., Breeze, Stench).
Formal Language & Logic
- Formal languages consist of defined structures with strict syntax and semantics.
- Grammar defines formation rules, while semantics determines sentence meanings and truth values.
Entailment and Models
- Knowledge base (KB) entails a sentence 𝛼 if 𝛼 is true in all worlds where KB is true, denoted as KB |= 𝛼.
- A theorem states that 𝛼 entails φ if models of 𝛼 are subsets of models of φ.
Back to Wumpus World
- The agent's percepts guide exploration of unexplored adjacent squares for safety checks against pits.
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Description
This quiz explores problem-solving agents in artificial intelligence, focusing on techniques like Breadth-First Search, Depth-First Search, and Uniform-Cost Search. Participants will learn about different types of problems and the formulation needed to address them effectively. Test your knowledge in AI problem-solving strategies!
