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Chapter 12 Planning and Acting in the Real World Ch 11 Issue: STRIPS - - PowerPoint PPT Presentation
Chapter 12 Planning and Acting in the Real World Ch 11 Issue: STRIPS - - PowerPoint PPT Presentation
Chapter 12 Planning and Acting in the Real World Ch 11 Issue: STRIPS talks about what actions to do, but not about how long it takes or even when actions should occur. STRIPS is a bit like playing dominoes Considering time is more
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[x,y] = earliest & latest possible start times. Critical path Duration
Fig 12-2 A solution to the auto job-shop scheduling problem.
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Resource:
Fig 12-4 A solution to the job shop scheduling problem with resources shown. Resources are reusable or consumable. Resources often are aggregated, like Inspectors(2). Sample algorithm used (p. 421) – “Minimum slack” – Considers the unscheduled actions that have had all their predecessors scheduled and schedules the one with the least slack for the earliest possible start. A “greedy” algorithm. Book takes a “plan first, schedule later” approach (p. 422). In “real world” the planning part may be done by humans, the scheduling by an algorithm.
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Better for complex plans and schedules – Hierarchical decomposition (p. 422) into a “Hierarchical Task Network” (HTN): At each “level,” only a small number of individual planning actions, then descend to lower levels to “solve these” for real. Fig 12-5 One possible decomposition for the BuildHouse action. At higher levels, the planner ignores “internal effects” of decompositions (p. 425). But these have to be resolved at some level…
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Fig 12-7 Decomposition of a high-level action within an existing plan.
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Nothing’s perfect, including HTN planning (p. 427) –
- 1. Need to rule out recursion – so that HTN plans are of
finite length and can be enumerated.
- 2. Need to bound the length of solutions. Avoid loops.
- 3. Adopt the hybrid approach that combines POP and HTN planning.
Partial-order-planning (Ch 11) decides whether a plan exists. Then plans can be sophisticated, like --
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O Henry seems perplexed by
- ur planner!
Fig 12-8 The Gift of the Magi problem – inconsistent states decomposed into a consistent solution!
Right – The O Henry short story made into a play…
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This was all “classical planning” so far. Tomorrow – We’ll talk about planning when you have “issues” like incomplete and incorrect information. (pp. 431 +) Some of the variations –
- Sensorless planning – using coercion
- Conditional planning
- Execution monitoring and replanning
- Continuous planning
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Consider the book’s “painting a table and chair the same color” plan, with these planning variations –
- Sensorless planning – using coercion
- Conditional planning
- Execution monitoring and replanning
- Continuous planning
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Conditional Planning
The “double Murphy” vacuum scenario. Or, “What’s the opposite of Eureka!”?
Fig 12 – 9 The first two levels of the search tree for the “double Murphy” vacuum cleaner world. (Cleaner sometimes deposits dirt when it moves to a clean destination square, and sometimes deposits dirt when Suck is applied to a clean square.)
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Fig 12 – 11 The first level of the search graph for the “triple Murphy” vacuum cleaner world. (Sometimes the vacuum cleaner also won’t move.)
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Conditional Planning In Partially Observable Environments
Multiplies the possibilities!
Fig 12 – 12 Part of the AND-OR graph for the “alternate double Murphy” vacuum cleaner world. (You can leave dirt behind when you move.)
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Execution Monitoring and Replanning
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Fig 12 – 14 Before execution, the planner comes up with a plan … then repaired. Above – In a replanning exercise, little bots at Stanford maneuver out of the way of an incoming obstacle. From robotics.stanford.edu/~dyhsu/ projects/rand/kinodyn.html
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Continuous Planning
Here the planning agent persists indefinitely…
Fig 12 – 15 The sequence of states as the continuous planning agent tries to reach the goal state. Left – Helsinki metro, where maintenance planning is a continuous process.
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Figs 12 – 16 / 18 Successive plans constructed by the continuous planning agent.
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Figs 12 – 19 / 21 Successive plans constructed by the continuous planning agent, cntd.
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Multiple agents… As in games, only they can cooperate, too! But, How do you coordinate the agents’ plans?
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