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Coverage Score
1
Mentioned Chunks
68
Mentioned Docs
2
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
near
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: near | CO_OCCURS | Propositional Logic | 16 |
| Auto Topic: near | CO_OCCURS | Problem Formulation | 8 |
| Auto Topic: near | CO_OCCURS | Utility Theory | 7 |
| Auto Topic: near | CO_OCCURS | State-Space Search | 6 |
| Auto Topic: near | CO_OCCURS | Informed Search | 6 |
| Auto Topic: hash | CO_OCCURS | Auto Topic: near | 6 |
| Auto Topic: near | CO_OCCURS | Logical Agents | 5 |
| Auto Topic: bin | CO_OCCURS | Auto Topic: near | 4 |
| Auto Topic: near | CO_OCCURS | Inference | 3 |
| Auto Topic: near | CO_OCCURS | Auto Topic: polynomial | 3 |
| Auto Topic: international | CO_OCCURS | Auto Topic: near | 3 |
| Auto Topic: ethics | CO_OCCURS | Auto Topic: near | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... ghbors using a hash table, when hash codes rely on anexact match? Hash codes randomly distribute values among the bins, but we want to have near points grouped together in the same bin; we want a locality-sensitive hash (LSH). Locality-sensitive hash We can’t use hashes to solve ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | near neighbors, we will need a hash function g(x) that has the property that, for any two points x j and x j′, the probability that they have the same hash code is small if their distance is more than cr , and is high if their distance is less than r. For simplicity we will treat ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... tten in linear form as [S [NP [Article every] [Noun wumpus]][VP [Verbsmells]]]. Section 24.3 Parsing 889 I detect the Adjective wumpus near Pronoun I detect the smelly wumpus near me Pronoun NP S VP V erb NP Article Prep Noun NP PP NP Adjs me smelly Figure 24.7 A dependency-style ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... e the actual distance to xq for each of the points in C and return the k closest points. With high probability, each of the points that are near to xq will show up in at least one of the bins, and although some far-away points will show up as well, we can ignore those. With large ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... be classified as “thermoelectric” or “topological insulator,” their model is able to answer correctly. For example, CsAgGa2Se4 never appears near “thermoelectric” in the corpus, but it does appear 924 Chapter 25 Deep Learning for Natural Language Processing near “chalcogenide,” “b ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ess and Ostrom, 2007). Ray Kurzweil (2005) proclaimed The Singularity is Near , and a decade later Murray Shanahan (2015) gave an update on the topic. Microsoft cofounder Paul Allen countered with The Singularity isn’t Near (2011). He didn’t dispute the possibility of ultraintell ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... and Ng, A. Y . (2000). Ap- proximate planning in large POMDPs via reusable tra- jectories. In NeurIPS 12. Kearns, M. and Singh, S. (1998). Near-optimal rein- forcement learning in polynomial time. In ICML-98. Kearns, M. and Vazirani, U. (1994). An Introduction to Computational L ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... h on a binary search tree, where the solution is found on the fourth iteration. Iterative deepening search may seem wasteful because states near the top of the search tree are re-generated multiple times. But for many state spaces, most of the nodes are in the bottom level, so it ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... k to make good use of available memory, and the algorithm executes fast because it expands fewer nodes. For many prob- lems it can find good near-optimal solutions. You can think of uniform-cost or A ∗ search as spreading out everywhere in concentric contours, and think of beam se ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... the table. Usually after about 10 or 15 moves we end up in a rarely seen position, and the program must switch from table lookup to search. Near the end of the game there are again fewer possible positions, and thus it is easier to do lookup. But here it is the computer that has ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... Sheppard (2002). Video games such as StarCraft II involve hundreds of partially observable units moving in real time with high-dimensional near-continuous6 observation and action spaces with com- plex rules. Oriol Vinyals, who was Spain’s StarCraft champion at age 15, described h ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | gh-dimensional near-continuous6 observation and action spaces with com- plex rules. Oriol Vinyals, who was Spain’s StarCraft champion at age 15, described how the game can serve as a testbed and grand challenge for reinforcement learning (Vinyals et al., 2017a). In 2019, Vinyals ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... opagation (Parisi and Zecchina, 2002; Maneva et al., 2007) Survey propagation take advantage of special properties of random SAT instances near the satisfiability |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... ropagation (Parisi and Zecchina, 2002; Maneva et al., 2007) Survey propagation take advantage of special properties of random SAT instances near the satisfiability threshold and greatly outperform general SAT solvers on such instances. The current state of theoretical understandin ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... space. In the example given above, the occasional use of WEIGHTED -SAMPLE to restart the chain in a new state serves this purpose. Besides near-complete freedom in designing proposal distributions, MH has two addi- tional properties that make it practical. First, the posterior p ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... needs a hotel room for her next business meeting in Geneva. Robbie can act now— let’s say he can book Harriet into a very expensive hotel near the meeting venue. He is quite unsure how much Harriet will like the hotel and its price; let’s say he has a uniform probability for its ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... hapter 3). There are several reasons why additive discounted rewards make sense. One is empirical: both humans and animals appear to value near-term rewards more highly than rewards in the distant future. Another is economic: if the rewards are monetary, then it really is better ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... the sum of rewards beyond H is bounded by γHRmax/(1 −γ), a depth of H = ⌈logγϵ(1 −γ)/Rmax⌉ suffices. So, building a tree to this depth gives near-optimal decisions. For example, with γ =0.5, ϵ =0.1, and Rmax =1, we find H =5, which seems reasonable. On the other hand, ifγ =0.9, H = ... |