Auto Topic: pie
auto_pie | topic
Coverage Score
1
Mentioned Chunks
10
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
pie
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: pie | CO_OCCURS | Auto Topic: ultimatum | 3 |
| Auto Topic: pie | CO_OCCURS | Constraint Satisfaction Problem | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | is a pair (x,1− x), where x is the amount of the pie that A1 gets, and 1− x is the amount that A2 gets. The space of possible deals (the negotiation set) is thus: Negotiation set {(x,1− x) : 0≤ x≤ 1}. 632 Chapter 17 Multiagent Decision Making Now, how should agents negotiate in t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | le pie. Thus, these two strategies— A1 proposes to get the whole pie, and A2 accepts—form a Nash equilibrium. Now consider the case where we permit exactly two rounds of negotiation. Now the power has shifted: A2 can simply reject the first offer, thereby turning the game into a o ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... come—any outcome—in finite time rather than being stuck in the infinitely time-consuming conflict deal. We will use the scenario of dividing a pie to illustrate alternating offers. The idea is that there is some resource (the “pie”) whose value is 1, which can be divided into two pa ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... ase has the same analysis as given above: we simply have an ultimatum game. With two rounds the situation changes, because the value of the pie reduces in accor- dance with discount factorsγi. Suppose A2 rejects A1’s initial proposal. Then A2 will get the whole pie with an ultima ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | will get the whole pie with an ultimatum in the second round. But thevalue of that whole pie has reduced: it is only worthγ2 to A2. Agent A1 can take this fact into account by offering (1−γ2,γ 2), an Section 17.4 Making Collective Decisions 633 offer that A2 may as well accept be ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... s good if the landmarks are spread around the perimeter of the graph. Thus, a good technique is to find the centroid of the graph, arrange k pie-shaped wedges around the centroid, and in each wedge select the vertex that is farthest from the center. Landmarks work especially well ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | k pie-shaped wedges around the centroid, and in each wedge select the vertex that is farthest from the center. Landmarks work especially well in route-finding problems because of the way roads are laid out in the world: a lot of traffic actually wants to travel between landmarks, s ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... scount factor γi (see page 555) for each agent (0 ≤γi < 1). Suppose that at some point in the negotiation agent i is offered a slice of the pie of size x. The value of the slice x at time t is γt i x. Thus on the first negotiation step (time 0), the value is γ0 i x = x, and at any ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... me probability to each parse. We would like a grammar that prefers the parses “[[spaghetti and meatballs] or lasagna]” and “[spaghetti and [pie or cake]]” over the alternative bracketing for each of these phrases. A lexicalized PCFG is a type of augmented grammar that allows us t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... 81 categorical, 409 conditional, nonparametric, 440 cumulative, 531, 1078 heavy-tailed, 160 mixture, 790 Dittmer, S., 550, 1093 dividing a pie, 631 Dix, J., 360, 1089 DLV (logic programming system), 360 DNA, 134 Do, M., 47, 399, 1086, 1093 Do, M. B., 401, 1093 do-calculus, 470 Do ... |