Algorithm Evaluation Criteria
algorithm_evaluation_criteria | topic
Coverage Score
1
Mentioned Chunks
289
Mentioned Docs
17
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
data structuresprocessestime complexityspace complexitycompletenesscost-optimalityheuristicspros and cons
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Algorithm Evaluation Criteria | CO_OCCURS | Inference | 78 |
| Algorithm Evaluation Criteria | CO_OCCURS | Propositional Logic | 70 |
| Algorithm Evaluation Criteria | CO_OCCURS | State-Space Search | 64 |
| Algorithm Evaluation Criteria | CO_OCCURS | Constraint Satisfaction Problem | 63 |
| Algorithm Evaluation Criteria | CO_OCCURS | Informed Search | 63 |
| Algorithm Evaluation Criteria | CO_OCCURS | Problem Formulation | 62 |
| Algorithm Evaluation Criteria | CO_OCCURS | Logical Agents | 56 |
| Algorithm Evaluation Criteria | CO_OCCURS | Heuristic Function | 29 |
| Algorithm Evaluation Criteria | CO_OCCURS | Resolution | 27 |
| Algorithm Evaluation Criteria | CO_OCCURS | Uninformed Search | 23 |
| Algorithm Evaluation Criteria | CO_OCCURS | Breadth-First Search | 17 |
| Admissible Heuristic | CO_OCCURS | Algorithm Evaluation Criteria | 16 |
| A* Search | CO_OCCURS | Algorithm Evaluation Criteria | 16 |
| Algorithm Evaluation Criteria | CO_OCCURS | Task Environment | 15 |
| Algorithm Evaluation Criteria | CO_OCCURS | Uniform-Cost Search | 13 |
| Algorithm Evaluation Criteria | CO_OCCURS | Utility Theory | 12 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: international | 11 |
| Algorithm Evaluation Criteria | CO_OCCURS | Backtracking Search | 10 |
| Algorithm Evaluation Criteria | CO_OCCURS | Bidirectional Search | 10 |
| Algorithm Evaluation Criteria | CO_OCCURS | Making Simple Decisions | 9 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: conference | 9 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: polynomial | 7 |
| Algorithm Evaluation Criteria | CO_OCCURS | Iterative Deepening Search | 7 |
| Algorithm Evaluation Criteria | CO_OCCURS | Alpha-Beta Pruning | 7 |
| Algorithm Evaluation Criteria | CO_OCCURS | Rational Agent | 6 |
| Algorithm Evaluation Criteria | CO_OCCURS | Expectimax | 6 |
| Algorithm Evaluation Criteria | CO_OCCURS | Depth-First Search | 6 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: simon | 5 |
| Algorithm Evaluation Criteria | CO_OCCURS | Depth-Limited Search | 5 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: rbfs | 5 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: import | 5 |
| Adversarial Search | CO_OCCURS | Algorithm Evaluation Criteria | 5 |
| Algorithm Evaluation Criteria | CO_OCCURS | Consistent Heuristic | 4 |
| Algorithm Evaluation Criteria | CO_OCCURS | Minimax | 4 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: self | 4 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: row | 4 |
| AC-3 | CO_OCCURS | Algorithm Evaluation Criteria | 4 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: automation | 3 |
| Algorithm Evaluation Criteria | CO_OCCURS | Auto Topic: genetic | 3 |
| Algorithm Evaluation Criteria | CO_OCCURS | Minimum Remaining Values | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
exam_requirements Exam_Requirements.txt | 0.93 | 8 | ... or the algorithms presented in lectures and implemented in homework assignments, it is advisable to develop a deeper understanding of their data structures, processes, time complexity, space complexity, pros and cons, completeness, cost-optimality, heuristics, etc. For the algori ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.81 | 7 | ... ious search algorithms, we will consider the criteria used to choose among them. We can evaluate an algorithm’s performance in four ways: • Completeness: Is the algorithm guaranteed to find a solution when there is one, and to Completeness correctly report failure when there is no ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.79 | 6 | algorithms are judged on the basis of completeness, cost optimality, time com- plexity, and space complexity. • Uninformed search methods have access only to the problem definition. Algorithms build a search tree in an attempt to find a solution. Algorithms differ based on which no ... |
module_resources Module Resources/Module-2---Resources (1)_unzipped/03-Search-Problems.pptx.pdf | 0.75 | 4 | ... egy = order of tree expansion • Implemented by different queue structures (LIFO, FIFO, priority) Dimensions for evaluation • Completeness- always find the solution? • Optimality - finds a least cost solution (lowest path cost) first? • Time complexity - # of nodes generated (worst ca ... |
module_resources Module Resources/Module-2---Resources (1)_unzipped/03-Search-Problems.pptx.pdf | 0.7 | 5 | worst case) Time/space complexity variables • b, maximum branching factor of search tree • d, depth of the shallowest goal node • m, maximum length of any path in the state space (potentially ∞) 27 Introduction to space complexity You know about: • “Big O” notation • Time complex ... |
module_resources Module Resources/Module-2---Resources (1)_unzipped/03-Search-Problems.pptx.pdf | 0.68 | 4 | ... e first solution is the last node at depth 3: Total nodes constructed: 3*b nodes + 2*b2 nodes + 1*b3 nodes 42 43 ID search, Evaluation: Time Complexity • More generally, the time complexity is •(d)b + (d-1)b2 + … + (1)bd = O(bd) As efficient in terms of O(…) as Breadth First Search: ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... h in which we supply a depth limit,ℓ, and treat all nodesDepth-limited search at depthℓ as if they had no successors (see Figure 3.12). The time complexity is O(bℓ) and the space complexity is O(bℓ). Unfortunately, if we make a poor choice for ℓ the algorithm will fail to reach t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... it one more node. Section 3.5 Informed (Heuristic) Search Strategies 113 RBFS is optimal if the heuristic function h(n) is admissible. Its space complexity is linear in the depth of the deepest optimal solution, but its time complexity is rather difficult to characterize: it depen ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... nal structure (although we introduced features of states when it came time to do learning). • Search algorithms are judged on the basis of completeness, cost optimality, time com- plexity, and space complexity. • Uninformed search methods have access only to the problem definition ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... y Searching noted that several well-known problems contained exponentially many nodes on optimal-cost solution paths, implying exponential time complexity for A∗. There are many variations on the A ∗ algorithm. Pohl (1970) introduced weighted A ∗ search, and later a dynamic versi ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... complete depth-first exploration of the game tree. If the maximum depth of the tree is m and there are b legal moves at each point, then the time complexity of the minimax algorithm is O(b m). The space complexity is O(bm) for an algorithm that generates all actions at once, or O( ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... amine. Of course, “finitely many” is not always the same as “few.” IfKB andα contain n symbols in all, then there are 2 n models. Thus, the time complexity of the algorithm is O(2n). (The space complexity is only O(n) because the enumeration is depth-first.) Later in this chapter w ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... the size of the proof. It also means that back- ward chaining (unlike forward chaining) suffers from problems with repeated states and in- completeness. Despite these limitations, backward chaining has proven to be popular and effective in logic programming languages. 9.4.2 Logi ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... ilar in structure to the back- tracking algorithm for solving CSPs (Figure 5.5) and the DPLL algorithm for satisfiability (Figure 7.17). Its space complexity is only linear in the number of variables: the algorithm sums over the full joint distribution without ever constructing it ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... ny applica- tions that deal with sequences of noisy observations. As described so far, it has two practical drawbacks. The first is that its space complexity can be too high when the state space is large Section 14.2 Inference in Temporal Models 489 and the sequences are long. It ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... tions or different outcomes should get different explanations. It is quite easy to give a decision algorithm access to its own deliberative processes, simply by recording them and making them available as data structures. This means that machines may eventually be able to give be ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.64 | 2 | ... tic and weighted gram- mars. Information and Control, 15, 529–544. Samadi, M., Felner, A., and Schaeffer, J. (2008). Learning from multiple heuristics. In AAAI-08. Samet, H. (2006). Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann. Sammut, C., Hurst, S. ... |
module_resources Module Resources/Module-2---Resources (1)_unzipped/03-Search-Problems.pptx.pdf | 0.64 | 2 | ... nserted into queue only after testing to see if it is a goal state 31 Properties of breadth-first search Complete? Yes (if b is finite) Time Complexity? 1+b+b2+b3+… +bd = O(bd) Space Complexity? O(bd) (keeps every node in memory) Optimal? Yes, if cost = 1 per step (not optimal in g ... |