Auto Topic: chair
auto_chair | topic
Coverage Score
1
Mentioned Chunks
23
Mentioned Docs
1
Required Dimensions
definitionpros_cons
Covered Dimensions
definitionpros_cons
Keywords
chair
Relations
| Source | Type | Target | W |
|---|---|---|---|
| Auto Topic: chair | CO_OCCURS | Constraint Satisfaction Problem | 5 |
| Auto Topic: chair | CO_OCCURS | Task Environment | 4 |
| Auto Topic: chair | CO_OCCURS | Propositional Logic | 3 |
| Auto Topic: chair | CO_OCCURS | Problem Formulation | 3 |
Evidence Chunks
| Source | Confidence | Mentions | Snippet |
|---|---|---|---|
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.65 | 6 | ... y in O. The agent then replans for the minimal repair plus continuation to reach G. Now let’s return to the example problem of achieving a chair and table of matching color. Suppose the agent comes up with this plan: [LookAt(Table),LookAt(Chair), if Color(Table,c) ∧ Color(Chair,c ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... observable painting problem with the percept schemas given earlier, one possible conditional solution is as follows: [LookAt(Table),LookAt(Chair), if Color(Table,c) ∧ Color(Chair,c) then NoOp else [RemoveLid(Can1),LookAt(Can1),RemoveLid(Can2),LookAt(Can2), if Color(Table,c) ∧ Co ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | aint are white and the chair is black. It then executes Paint(Chair,Can1). At this point a classical planner would declare victory; the plan has been executed. But an online execution monitoring agent needs to check that the action succeeded. Suppose the agent perceives that the ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.63 | 5 | ... . For example, suppose there is no black or white paint, and the agent constructs a plan to solve the painting problem by painting both the chair and table red. Suppose that there is only enough red paint for the chair. With action monitoring, the agent would go ahead and paint t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | to the initial belief state b0, we get b1 = Color(x,C(x)) ∧ Open(Can1). When we apply the action Paint(Chair,Can1), the precondition Color(Can1,c) is satisfied by the literal Color(x,C(x)) with binding {x/Can1,c/C(Can1)} and the new belief state is b2 = Color(x,C(x)) ∧ Open(Can1) ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.61 | 4 | okAt(Can1),RemoveLid(Can2),LookAt(Can2), if Color(Table,c) ∧ Color(can,c) then Paint(Chair,can) else if Color(Chair,c) ∧ Color(can,c) then Paint(Table,can) else [Paint(Chair,Can1),Paint(Table,Can1)]]] Section 11.5 Planning and Acting in Nondeterministic Domains 389 Variables in t ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | ... planning and replanning for unknown environments. This will allow us to tackle sizable real-world problems. Consider this problem: given a chair and a table, the goal is to have them match—have the same color. In the initial state we have two cans of paint, but the colors of the ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.59 | 3 | both chair and table green. A plan-monitoring agent can detect failure whenever the current state is such that the remaining plan no longer works. Thus, it would not waste time painting the chair red. 392 Chapter 11 Automated Planning Plan monitoring achieves this by checking the ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... cience from Stanford in 1986. He then joined the faculty of the University of California at Berke- ley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he re ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... es divide up the world differently. The French have two words “chaise” and “fauteuil,” for a concept that English speakers cover with one: “chair.” But English speakers can easily recognize the categoryfauteuil and give it a name—roughly “open-arm chair”—so does language really m ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ” for a concept that English speakers cover with one: “chair.” But English speakers can easily recognize the categoryfauteuil and give it a name—roughly “open-arm chair”—so does language really make a difference? Whorf relied mainly on intuition and speculation, and his ideas hav ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | olors of the paint and the furniture are unknown. Only the table is initially in the agent’s field of view: Init(Object(Table) ∧ Object(Chair) ∧ Can(C1) ∧ Can(C2) ∧ InView(Table)) Goal(Color(Chair,c) ∧ Color(Table,c)) There are two actions: removing the lid from a paint can and pa ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.57 | 2 | ... mas at all. Note that even a sensorless agent can solve the painting problem. One solution is to open any can of paint and apply it to both chair and table, thus coercing them to be the same color (even though the agent doesn’t know what the color is). A contingent planning agent ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... afternoon. A schedule that has Prof. R teaching at 2 p.m. would still be an allowable solution (unless Prof. R happens to be the department chair) but would not be an optimal one. Preference constraints can often be encoded as costs on individual variable assignments— for example ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... rrectness of its action schemas. Whereas a contingent planner simply assumes that the effects of an action always succeed—that painting the chair does the job—a replanning agent would check the result and make an additional plan to fix any unexpected failure, such as an unpainted ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... problem can ignore InView fluents because the agent has no sensors. Furthermore, we take as given the unchanging facts Object(Table) ∧Object(Chair) ∧Can(C1) ∧Can(C2) because these hold in every belief state. The agent doesn’t know the colors of the cans or the objects, or whether ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... sible worlds that satisfy the formula. Given this initial belief state, the following action sequence is a solution: [RemoveLid(Can1),Paint(Chair,Can1),Paint(Table,Can1)]. We now show how to progress the belief state through the action sequence to show that the final belief state ... |
textbook Artificial-Intelligence-A-Modern-Approach-4th-Edition.pdf | 0.55 | 1 | ... xample, if we apply RemoveLid(Can1) to the initial belief state b0, we get b1 = Color(x,C(x)) ∧ Open(Can1). When we apply the action Paint(Chair,Can1), the precondition Color(Can1,c) is satisfied by the literal Color(x,C(x)) with binding |