Terms
A* Search Algorithm – A best-first search algorithm that finds the shortest path by combining cost-to-
come and estimated cost-to-go.
Abstraction – The process of removing details from a representation to make problem-solving more
efficient.
Adversarial Search – A search problem where two or more agents with opposing goals interact, such
as in competitive games.
Artificial General Intelligence (AGI) – A form of AI capable of performing any intellectual task that a
human can.
Artificial Intelligence (AI) – The study and construction of intelligent agents, which are systems that
perceive their environment and take actions to maximize their chances of success.
Artificial Narrow Intelligence (ANI) – AI specialized in performing a limited set of tasks efficiently.
Artificial Superintelligence (ASI) – A hypothetical AI that surpasses human intelligence in all
aspects.
Automatic Assembly Sequencing – A problem where a robot must determine the correct order to
assemble parts.
Automated Reasoning – The capability of a system to derive conclusions and make decisions based
on stored knowledge.
Backtracking Search – A variant of DFS that remembers the last decision point and explores
alternatives.
Bayesian Network – A probabilistic model that represents variables and their conditional
dependencies using a directed acyclic graph.
Best-First Search – A search algorithm that selects nodes based on an evaluation function, often
prioritizing the most promising option.
Big Data – Large and complex datasets that require specialized methods for analysis.
Bidirectional Search – A search algorithm that simultaneously searches from the initial state and the
goal state.
Branching Factor – The number of successor nodes generated from each state.
Breadth-First Search (BFS) – A search strategy that expands all nodes at the current depth before
moving to the next.
Child Node / Successor Node – A node generated from a parent node by applying an action.
Cognitive Revolution – A shift in psychology that viewed mental processes as computational symbol
manipulation.
Clairvoyance – A type of ESP where a person is believed to perceive distant or unseen events.
Closed-Loop System – A system that monitors the environment while executing actions to adjust its
behavior.
Combinatorial Creativity – A form of creativity that arises by combining existing ideas in novel ways.
,Combinatorial Explosion – A situation where the number of possible solutions grows exponentially,
making computation infeasible.
Completeness – A property of a search algorithm that guarantees finding a solution if one exists.
Computationally Intractable – A problem is computationally intractable if no known algorithm can
solve it efficiently, usually requiring superpolynomial or exponential time (e.g., O(2ⁿ), O(n!)).
Computationally Tractable – A problem is considered computationally tractable if it can be solved
efficiently using an algorithm that runs in polynomial time (e.g., O(n), O(n²), O(n³)).
Computational Systems – Systems designed to process and manipulate data algorithmically.
Connectionist Approach – An AI approach based on neural networks and distributed
representations.
Continuous State Machine – A model used to describe systems that change in a continuous manner
rather than discrete steps.
Cost Optimality – A property that ensures the search algorithm finds the lowest-cost solution.
Cybersecurity – The application of AI in detecting and preventing cyber threats.
Cycle – A path in a search tree that returns to a previous state.
Decision Theory – A framework for making optimal decisions under conditions of uncertainty.
Deep Learning – A machine learning technique that utilizes multiple layers of artificial neural
networks to extract patterns and make decisions.
Deontological Ethics – A moral philosophy that emphasizes following rules or duties rather than
focusing on outcomes.
Depth-First Search (DFS) – A search strategy that expands the deepest node in the search tree first.
Depth-Limited Search – A DFS variant that limits the depth of the search.
Diameter – The longest shortest path in a state space.
Discrete State Machine – A computational model where the system transitions between distinct
states based on defined rules.
Early Goal Test – A method of checking for a goal state immediately upon generating a node.
ESP (Extrasensory Perception) – The claimed ability to gain information through means beyond the
known human senses, such as telepathy or clairvoyance.
Evaluation Function – A function used to determine which node to expand next in best-first search.
Expand – The process of generating child nodes from a parent node.
Exploratory Creativity – The process of discovering new concepts by exploring structured spaces of
possibilities.
FIFO Queue – A first-in-first-out queue used in breadth-first search.
Frontier – The set of nodes that have been generated but not yet expanded.
1
,Gamblers and Robbers Problem – A problem where individuals must cross a river while following
constraints to avoid negative consequences (used to illustrate search algorithms).
Generative Systems – Systems capable of creating new content, patterns, or behaviors through AI
models.
Goal Formulation – The process of defining a goal for an agent to achieve.
Graph – A mathematical representation of a state space where states are nodes and actions are
directed edges.
Graph Search – A search algorithm that checks for redundant paths.
Graph Separation Property – The principle that a frontier divides explored and unexplored states.
Grid World – A two-dimensional environment where agents navigate between grid cells.
Gödel’s Theorem – A mathematical theorem stating that within any sufficiently complex formal
system, there exist true statements that cannot be proven within that system.
Human-Level AI (HLAI) – AI that can perform any intellectual task that a human can do.
Information Retrieval – The process of obtaining relevant data from a large dataset, often used in AI
for search engines and knowledge retrieval.
Intractable Problems – Problems that, while solvable in theory, require infeasible time or space
resources to solve. Key resources are time (the solution takes too long to compute) and space (finding
the solution requires too much memory).
Intelligent Agent – An entity that receives percepts from the environment and performs actions.
Iterative Deepening Search – A search strategy that repeatedly applies depth-limited search with
increasing depth limits.
Jugs of Water Problem – A problem-solving task where water must be distributed between jugs to
reach a specified goal state.
Knowledge Representation – A way to store and organize information so that an AI system can use
it to reason and make decisions.
Knuth’s Conjecture – A mathematical problem where any integer is reached from 4 using factorial,
square root, and floor operations.
Late Goal Test – A method of checking for a goal state only when expanding a node.
Lethal Autonomous Weapons – AI-powered weapons capable of selecting and engaging targets
without human intervention.
Level of Abstraction – The degree of simplification in representing a problem.
LIFO Queue / Stack – A last-in-first-out queue used in depth-first search.
Local Search – A search method that focuses on finding an optimal solution by iteratively making
small changes to a candidate solution.
Logicist Approach – An AI paradigm based on formal logic and symbolic reasoning.
Machine Learning – A subfield of AI that studies the ability of machines to improve performance
based on experience.
2
,Machine Translation – The use of AI to translate text or speech from one language to another.
Markov Decision Process (MDP) – A mathematical framework for modeling decision-making in
situations where outcomes are partly random and partly under the control of a decision-maker.
Maze Search – A problem representation where an agent navigates through a maze to find the goal
state.
Narrow AI – Narrow AI, also known as weak AI, is an application of artificial intelligence technologies
to enable a high-functioning system that replicates—and perhaps surpasses—human intelligence for a
dedicated purpose.
Natural Language Processing (NLP) – The ability of a computer system to understand and process
human language.
Node – A data structure in a search tree that represents a state.
Open-Loop System – A system that executes a sequence of actions without monitoring the
environment.
Optimal Solution – A solution with the lowest path cost among all possible solutions.
Parent Node – A node that generated another node.
Path – A sequence of states connected by actions.
Physical Symbol System Hypothesis – The hypothesis that intelligence arises from manipulating
symbols in a physical system.
Polynomial Time Complexity – Refers to algorithms that have a worst-case runtime expressed as a
polynomial function of the input size (e.g., O(n²), O(n³)). These are considered efficient.
Priority Queue – A queue where nodes are removed based on priority (e.g., lowest cost).
Problem Formulation – The process of deciding what actions and states to consider in the search for
a solution.
Problem-Solving Agent – An agent that decides on a sequence of actions to achieve a goal by
performing search.
Problem-Solving Search – A computational process used by problem-solving agents to find
sequences of actions leading to a goal.
Protein Design – A search problem where a sequence of amino acids is optimized to form a desired
protein structure.
Psychokinesis – The alleged ability to influence objects or events using the mind alone.
Psychological AI – AI that aims to replicate human cognitive processes.
Queue – A data structure used to manage the frontier in search algorithms.
Rational AI – An approach that emphasizes decision-making based on logic and probability rather
than human-like cognition.
Rational Agent – An agent that acts to achieve the best outcome or the best expected outcome
under uncertainty.
Rationality – The concept of doing the "right thing" based on expected outcomes and objectives.
3
,Reached – The set of states for which a node has been generated.
Real-World Problem – A practical problem where the search formulation varies depending on real-
world constraints.
Redundant Path – A path that leads to the same state but is more costly than another path.
Reinforcement Learning – A type of machine learning where an agent learns to make decisions by
receiving rewards or penalties.
Repeated State – A state that appears more than once in a search tree.
Robot Navigation – A search problem where an agent must plan its path in a physical environment.
Search Algorithm – A method used to find a solution to a search problem.
Search Problem – A formal representation of a problem consisting of:
● State space – The set of all possible states the environment can be in.
● Initial state – The state the agent starts in.
● Goal state(s) – The state(s) that the agent aims to reach.
● Actions – The set of available actions in each state.
● Transition model – A function describing the result of applying an action to a state.
● Action cost function – A function defining the cost associated with applying an action.
Separator – A boundary between explored and unexplored states in a search algorithm.
Simultaneous Localization and Mapping (SLAM) – A computational method where an agent
constructs a map of an unknown environment while tracking its own location.
Singularity – A hypothetical point where artificial intelligence surpasses human intelligence, leading
to unpredictable consequences.
Sliding-Tile Puzzle – A puzzle where tiles must be moved into a goal configuration (e.g., 8-puzzle,
15-puzzle).
Sokoban Puzzle – A grid world problem where an agent must push boxes to designated locations.
Solution – A sequence of actions that leads from the initial state to a goal state.
Solipsism – The philosophical idea that only one's mind is sure to exist, making it difficult to assess
whether other minds (or machines) are truly conscious.
Space Complexity – The memory required for a search algorithm.
Standardized Problem – A well-defined problem used for benchmarking search algorithms.
State Representation – A structured way to define a problem’s state space, including variables and
their values.
State-Space Graph – A representation of all possible states and the transitions between them.
Strong AI – The idea that AI can achieve human-like understanding and consciousness.
Superpolynomial Time Complexity – Refers to algorithms that require time that grows faster than
any polynomial function, such as exponential (O(2ⁿ)) or factorial (O(n!)) time, making them
impractical for large inputs.
Systematic Search – A search strategy that ensures all states are eventually reached.
4
, Telepathy – A form of ESP where direct communication occurs between minds without using sensory
channels.
Terminal State – A state in a search space or game tree that has no successors. It represents an end
state where no further moves or actions are possible.
Time Complexity – The computational time required for a search algorithm.
Transformational Creativity – The most advanced form of creativity where the rules or constraints of
a domain are altered to generate novel ideas.
Traveling Salesperson Problem (TSP) – A problem where an agent must visit every city in a
network at the lowest cost.
Tree-Like Search – A search algorithm that does not check for redundant paths.
Turing Test – A thought experiment proposed by Alan Turing to evaluate a machine’s intelligence by
testing whether a human can distinguish its responses from those of another human.
Uncomputable Problems – Problems that cannot be solved by any algorithm or computational
system.
problems are provably uncomputable
Uniform-Cost Search – A search algorithm that expands the node with the lowest path cost.
Uninformed Search – A search strategy that does not use domain knowledge.
Universal Computation – The principle that any computational problem solvable by one Turing-
complete system can be solved by any other.
Universal Machine – A concept introduced by Alan Turing, referring to a machine capable of
simulating any other Turing machine (i.e., a general-purpose computer).
Utilitarianism – A principle in decision-making that seeks to maximize overall happiness or utility.
VLSI Layout Problem – A problem of placing components on an integrated circuit chip to minimize
space and wiring complexity.
Von Neumann Architecture – A computer architecture model that separates memory, processing,
and control units.
Weak AI – AI designed for specific tasks without true understanding or consciousness.
5
A* Search Algorithm – A best-first search algorithm that finds the shortest path by combining cost-to-
come and estimated cost-to-go.
Abstraction – The process of removing details from a representation to make problem-solving more
efficient.
Adversarial Search – A search problem where two or more agents with opposing goals interact, such
as in competitive games.
Artificial General Intelligence (AGI) – A form of AI capable of performing any intellectual task that a
human can.
Artificial Intelligence (AI) – The study and construction of intelligent agents, which are systems that
perceive their environment and take actions to maximize their chances of success.
Artificial Narrow Intelligence (ANI) – AI specialized in performing a limited set of tasks efficiently.
Artificial Superintelligence (ASI) – A hypothetical AI that surpasses human intelligence in all
aspects.
Automatic Assembly Sequencing – A problem where a robot must determine the correct order to
assemble parts.
Automated Reasoning – The capability of a system to derive conclusions and make decisions based
on stored knowledge.
Backtracking Search – A variant of DFS that remembers the last decision point and explores
alternatives.
Bayesian Network – A probabilistic model that represents variables and their conditional
dependencies using a directed acyclic graph.
Best-First Search – A search algorithm that selects nodes based on an evaluation function, often
prioritizing the most promising option.
Big Data – Large and complex datasets that require specialized methods for analysis.
Bidirectional Search – A search algorithm that simultaneously searches from the initial state and the
goal state.
Branching Factor – The number of successor nodes generated from each state.
Breadth-First Search (BFS) – A search strategy that expands all nodes at the current depth before
moving to the next.
Child Node / Successor Node – A node generated from a parent node by applying an action.
Cognitive Revolution – A shift in psychology that viewed mental processes as computational symbol
manipulation.
Clairvoyance – A type of ESP where a person is believed to perceive distant or unseen events.
Closed-Loop System – A system that monitors the environment while executing actions to adjust its
behavior.
Combinatorial Creativity – A form of creativity that arises by combining existing ideas in novel ways.
,Combinatorial Explosion – A situation where the number of possible solutions grows exponentially,
making computation infeasible.
Completeness – A property of a search algorithm that guarantees finding a solution if one exists.
Computationally Intractable – A problem is computationally intractable if no known algorithm can
solve it efficiently, usually requiring superpolynomial or exponential time (e.g., O(2ⁿ), O(n!)).
Computationally Tractable – A problem is considered computationally tractable if it can be solved
efficiently using an algorithm that runs in polynomial time (e.g., O(n), O(n²), O(n³)).
Computational Systems – Systems designed to process and manipulate data algorithmically.
Connectionist Approach – An AI approach based on neural networks and distributed
representations.
Continuous State Machine – A model used to describe systems that change in a continuous manner
rather than discrete steps.
Cost Optimality – A property that ensures the search algorithm finds the lowest-cost solution.
Cybersecurity – The application of AI in detecting and preventing cyber threats.
Cycle – A path in a search tree that returns to a previous state.
Decision Theory – A framework for making optimal decisions under conditions of uncertainty.
Deep Learning – A machine learning technique that utilizes multiple layers of artificial neural
networks to extract patterns and make decisions.
Deontological Ethics – A moral philosophy that emphasizes following rules or duties rather than
focusing on outcomes.
Depth-First Search (DFS) – A search strategy that expands the deepest node in the search tree first.
Depth-Limited Search – A DFS variant that limits the depth of the search.
Diameter – The longest shortest path in a state space.
Discrete State Machine – A computational model where the system transitions between distinct
states based on defined rules.
Early Goal Test – A method of checking for a goal state immediately upon generating a node.
ESP (Extrasensory Perception) – The claimed ability to gain information through means beyond the
known human senses, such as telepathy or clairvoyance.
Evaluation Function – A function used to determine which node to expand next in best-first search.
Expand – The process of generating child nodes from a parent node.
Exploratory Creativity – The process of discovering new concepts by exploring structured spaces of
possibilities.
FIFO Queue – A first-in-first-out queue used in breadth-first search.
Frontier – The set of nodes that have been generated but not yet expanded.
1
,Gamblers and Robbers Problem – A problem where individuals must cross a river while following
constraints to avoid negative consequences (used to illustrate search algorithms).
Generative Systems – Systems capable of creating new content, patterns, or behaviors through AI
models.
Goal Formulation – The process of defining a goal for an agent to achieve.
Graph – A mathematical representation of a state space where states are nodes and actions are
directed edges.
Graph Search – A search algorithm that checks for redundant paths.
Graph Separation Property – The principle that a frontier divides explored and unexplored states.
Grid World – A two-dimensional environment where agents navigate between grid cells.
Gödel’s Theorem – A mathematical theorem stating that within any sufficiently complex formal
system, there exist true statements that cannot be proven within that system.
Human-Level AI (HLAI) – AI that can perform any intellectual task that a human can do.
Information Retrieval – The process of obtaining relevant data from a large dataset, often used in AI
for search engines and knowledge retrieval.
Intractable Problems – Problems that, while solvable in theory, require infeasible time or space
resources to solve. Key resources are time (the solution takes too long to compute) and space (finding
the solution requires too much memory).
Intelligent Agent – An entity that receives percepts from the environment and performs actions.
Iterative Deepening Search – A search strategy that repeatedly applies depth-limited search with
increasing depth limits.
Jugs of Water Problem – A problem-solving task where water must be distributed between jugs to
reach a specified goal state.
Knowledge Representation – A way to store and organize information so that an AI system can use
it to reason and make decisions.
Knuth’s Conjecture – A mathematical problem where any integer is reached from 4 using factorial,
square root, and floor operations.
Late Goal Test – A method of checking for a goal state only when expanding a node.
Lethal Autonomous Weapons – AI-powered weapons capable of selecting and engaging targets
without human intervention.
Level of Abstraction – The degree of simplification in representing a problem.
LIFO Queue / Stack – A last-in-first-out queue used in depth-first search.
Local Search – A search method that focuses on finding an optimal solution by iteratively making
small changes to a candidate solution.
Logicist Approach – An AI paradigm based on formal logic and symbolic reasoning.
Machine Learning – A subfield of AI that studies the ability of machines to improve performance
based on experience.
2
,Machine Translation – The use of AI to translate text or speech from one language to another.
Markov Decision Process (MDP) – A mathematical framework for modeling decision-making in
situations where outcomes are partly random and partly under the control of a decision-maker.
Maze Search – A problem representation where an agent navigates through a maze to find the goal
state.
Narrow AI – Narrow AI, also known as weak AI, is an application of artificial intelligence technologies
to enable a high-functioning system that replicates—and perhaps surpasses—human intelligence for a
dedicated purpose.
Natural Language Processing (NLP) – The ability of a computer system to understand and process
human language.
Node – A data structure in a search tree that represents a state.
Open-Loop System – A system that executes a sequence of actions without monitoring the
environment.
Optimal Solution – A solution with the lowest path cost among all possible solutions.
Parent Node – A node that generated another node.
Path – A sequence of states connected by actions.
Physical Symbol System Hypothesis – The hypothesis that intelligence arises from manipulating
symbols in a physical system.
Polynomial Time Complexity – Refers to algorithms that have a worst-case runtime expressed as a
polynomial function of the input size (e.g., O(n²), O(n³)). These are considered efficient.
Priority Queue – A queue where nodes are removed based on priority (e.g., lowest cost).
Problem Formulation – The process of deciding what actions and states to consider in the search for
a solution.
Problem-Solving Agent – An agent that decides on a sequence of actions to achieve a goal by
performing search.
Problem-Solving Search – A computational process used by problem-solving agents to find
sequences of actions leading to a goal.
Protein Design – A search problem where a sequence of amino acids is optimized to form a desired
protein structure.
Psychokinesis – The alleged ability to influence objects or events using the mind alone.
Psychological AI – AI that aims to replicate human cognitive processes.
Queue – A data structure used to manage the frontier in search algorithms.
Rational AI – An approach that emphasizes decision-making based on logic and probability rather
than human-like cognition.
Rational Agent – An agent that acts to achieve the best outcome or the best expected outcome
under uncertainty.
Rationality – The concept of doing the "right thing" based on expected outcomes and objectives.
3
,Reached – The set of states for which a node has been generated.
Real-World Problem – A practical problem where the search formulation varies depending on real-
world constraints.
Redundant Path – A path that leads to the same state but is more costly than another path.
Reinforcement Learning – A type of machine learning where an agent learns to make decisions by
receiving rewards or penalties.
Repeated State – A state that appears more than once in a search tree.
Robot Navigation – A search problem where an agent must plan its path in a physical environment.
Search Algorithm – A method used to find a solution to a search problem.
Search Problem – A formal representation of a problem consisting of:
● State space – The set of all possible states the environment can be in.
● Initial state – The state the agent starts in.
● Goal state(s) – The state(s) that the agent aims to reach.
● Actions – The set of available actions in each state.
● Transition model – A function describing the result of applying an action to a state.
● Action cost function – A function defining the cost associated with applying an action.
Separator – A boundary between explored and unexplored states in a search algorithm.
Simultaneous Localization and Mapping (SLAM) – A computational method where an agent
constructs a map of an unknown environment while tracking its own location.
Singularity – A hypothetical point where artificial intelligence surpasses human intelligence, leading
to unpredictable consequences.
Sliding-Tile Puzzle – A puzzle where tiles must be moved into a goal configuration (e.g., 8-puzzle,
15-puzzle).
Sokoban Puzzle – A grid world problem where an agent must push boxes to designated locations.
Solution – A sequence of actions that leads from the initial state to a goal state.
Solipsism – The philosophical idea that only one's mind is sure to exist, making it difficult to assess
whether other minds (or machines) are truly conscious.
Space Complexity – The memory required for a search algorithm.
Standardized Problem – A well-defined problem used for benchmarking search algorithms.
State Representation – A structured way to define a problem’s state space, including variables and
their values.
State-Space Graph – A representation of all possible states and the transitions between them.
Strong AI – The idea that AI can achieve human-like understanding and consciousness.
Superpolynomial Time Complexity – Refers to algorithms that require time that grows faster than
any polynomial function, such as exponential (O(2ⁿ)) or factorial (O(n!)) time, making them
impractical for large inputs.
Systematic Search – A search strategy that ensures all states are eventually reached.
4
, Telepathy – A form of ESP where direct communication occurs between minds without using sensory
channels.
Terminal State – A state in a search space or game tree that has no successors. It represents an end
state where no further moves or actions are possible.
Time Complexity – The computational time required for a search algorithm.
Transformational Creativity – The most advanced form of creativity where the rules or constraints of
a domain are altered to generate novel ideas.
Traveling Salesperson Problem (TSP) – A problem where an agent must visit every city in a
network at the lowest cost.
Tree-Like Search – A search algorithm that does not check for redundant paths.
Turing Test – A thought experiment proposed by Alan Turing to evaluate a machine’s intelligence by
testing whether a human can distinguish its responses from those of another human.
Uncomputable Problems – Problems that cannot be solved by any algorithm or computational
system.
problems are provably uncomputable
Uniform-Cost Search – A search algorithm that expands the node with the lowest path cost.
Uninformed Search – A search strategy that does not use domain knowledge.
Universal Computation – The principle that any computational problem solvable by one Turing-
complete system can be solved by any other.
Universal Machine – A concept introduced by Alan Turing, referring to a machine capable of
simulating any other Turing machine (i.e., a general-purpose computer).
Utilitarianism – A principle in decision-making that seeks to maximize overall happiness or utility.
VLSI Layout Problem – A problem of placing components on an integrated circuit chip to minimize
space and wiring complexity.
Von Neumann Architecture – A computer architecture model that separates memory, processing,
and control units.
Weak AI – AI designed for specific tasks without true understanding or consciousness.
5
