CS 4700: Foundations of Artificial Intelligence Bart Selman Problem
... – time complexity: number of nodes generated – space complexity: maximum number of nodes in memory – optimality: does it always find a least-cost solution? Time and space complexity are measured in terms of – b: maximum branching factor of the search tree – d: depth of the least-cost solution – ...
... – time complexity: number of nodes generated – space complexity: maximum number of nodes in memory – optimality: does it always find a least-cost solution? Time and space complexity are measured in terms of – b: maximum branching factor of the search tree – d: depth of the least-cost solution – ...
Problem Solving and Search (part 1)
... 1. Goal Formulation: Set of one or more (desirable) world states (e.g. checkmate in chess). 2. Problem formulation: What actions and states to consider given a goal and an initial state. 3. Search for solution: Given the problem, search for a solution --- a sequence of actions to achieve the goal st ...
... 1. Goal Formulation: Set of one or more (desirable) world states (e.g. checkmate in chess). 2. Problem formulation: What actions and states to consider given a goal and an initial state. 3. Search for solution: Given the problem, search for a solution --- a sequence of actions to achieve the goal st ...
Introduction to Artificial Intelligence and Search problems
... • A: No, unless the search graph is un-weighted. • Q: Will IDS always find the same solution as BFS given that the nodes expansion order is deterministic? • A: Yes. Each iteration of IDS explores new nodes the same order a BFS does. ...
... • A: No, unless the search graph is un-weighted. • Q: Will IDS always find the same solution as BFS given that the nodes expansion order is deterministic? • A: Yes. Each iteration of IDS explores new nodes the same order a BFS does. ...
Document
... • A: No, unless the search graph is un-weighted. • Q: Will IDS always find the same solution as BFS given that the nodes expansion order is deterministic? • A: Yes. Each iteration of IDS explores new nodes the same order a BFS does. ...
... • A: No, unless the search graph is un-weighted. • Q: Will IDS always find the same solution as BFS given that the nodes expansion order is deterministic? • A: Yes. Each iteration of IDS explores new nodes the same order a BFS does. ...
KClustering
... that seeks to find a local minimum given an initial condition. In each iteration, we find a better local solution. We only include new centers that reduce the value of the objective function at each step. The algorithm converges when it is not possible to exchange any center such that the total cost ...
... that seeks to find a local minimum given an initial condition. In each iteration, we find a better local solution. We only include new centers that reduce the value of the objective function at each step. The algorithm converges when it is not possible to exchange any center such that the total cost ...
Mastering the game of Go with deep neural networks and tree search
... Figure 3: Monte-Carlo tree search in AlphaGo. a Each simulation traverses the tree by selecting the edge with maximum action-value Q, plus a bonus u(P ) that depends on a stored prior probability P for that edge. b The leaf node may be expanded; the new node is processed once by the policy network ...
... Figure 3: Monte-Carlo tree search in AlphaGo. a Each simulation traverses the tree by selecting the edge with maximum action-value Q, plus a bonus u(P ) that depends on a stored prior probability P for that edge. b The leaf node may be expanded; the new node is processed once by the policy network ...
Rollout Sampling Policy Iteration for Decentralized POMDPs
... quite limited in terms of the size of the problems they can tackle. This is not surprising given that finite-horizon DECPOMDPs are NEXP-complete [2]. Intuitively, the main reason is that it is hard to define a compact belief state and compute a value function for DEC-POMDPs, as is often done for POM ...
... quite limited in terms of the size of the problems they can tackle. This is not surprising given that finite-horizon DECPOMDPs are NEXP-complete [2]. Intuitively, the main reason is that it is hard to define a compact belief state and compute a value function for DEC-POMDPs, as is often done for POM ...
as a PDF
... to guess the assignment and test its feasibility, which can be done in linear time in the number of parallel and alternative subgraphs (and hence in the number of edges). For the NP-hardness, we shall show that the 3SAT problem, which is known to be NP-complete, can be reduced (in a polynomial time) ...
... to guess the assignment and test its feasibility, which can be done in linear time in the number of parallel and alternative subgraphs (and hence in the number of edges). For the NP-hardness, we shall show that the 3SAT problem, which is known to be NP-complete, can be reduced (in a polynomial time) ...
A Fast Arc Consistency Algorithm for n-ary Constraints Olivier Lhomme Jean-Charles R´egin
... is a support for (x6 , 4) is (4, 4, 4, 4, 4, 4). Thus, a valid support for (x1 , 0) is at least the smallest of the above four supports, i.e., (1, 1, 1, 1, 1, 1). This means that it is useless to check the validity of a support smaller than (1, 1, 1, 1, 1, 1). Obviously (1, 1, 1, 1, 1, 1) is not a s ...
... is a support for (x6 , 4) is (4, 4, 4, 4, 4, 4). Thus, a valid support for (x1 , 0) is at least the smallest of the above four supports, i.e., (1, 1, 1, 1, 1, 1). This means that it is useless to check the validity of a support smaller than (1, 1, 1, 1, 1, 1). Obviously (1, 1, 1, 1, 1, 1) is not a s ...
for taking notes
... optimality of solutions of Breadth-first search The algorithm performs successive depth-first searches with limited depth that is increased each iteration This strategy gives a behaviour similar to breadth-first search but without its spatial complexity because each exploration is depth-first, altho ...
... optimality of solutions of Breadth-first search The algorithm performs successive depth-first searches with limited depth that is increased each iteration This strategy gives a behaviour similar to breadth-first search but without its spatial complexity because each exploration is depth-first, altho ...
Mining Spatial Trends by a Colony of Cooperative Ant Agents
... resign from further extending it when the regression confidence of the current path becomes bellow the threshold. But this assumption is a restricting one, as it may mislead us by forcing a trend to stop from growth that would get much higher confidence if not blocked. Many solutions for NP-Complete ...
... resign from further extending it when the regression confidence of the current path becomes bellow the threshold. But this assumption is a restricting one, as it may mislead us by forcing a trend to stop from growth that would get much higher confidence if not blocked. Many solutions for NP-Complete ...
IJAI-13 - aut.upt.ro
... section four will present the results when these implementations are used to create the specified initial populations for a MOEA approach to segmentation, analyzing the final results of the chosen algorithm over the initial and final populations. Finally the conclusions which can be extracted from t ...
... section four will present the results when these implementations are used to create the specified initial populations for a MOEA approach to segmentation, analyzing the final results of the chosen algorithm over the initial and final populations. Finally the conclusions which can be extracted from t ...