The document discusses planning for problems with time, resources, and uncertainty. It covers job shop scheduling, hierarchical task network planning, conditional planning for non-deterministic domains, and techniques for monitoring plan execution and replanning if needed to handle unexpected outcomes or state changes. Key concepts include temporal planning with durations and timelines, resource constraints, hierarchical decomposition, conditional branches, belief states for partial observability, and replanning to repair or replace plans that fail during execution.

1. Artificial Intelligence 1: Planning in the real world Lecturer: Tom Lenaerts SWITCH, Vlaams Interuniversitair Instituut voor Biotechnologie

5. Solution found by POP Slack of 15 critical path

10. Car example with resources

14. Buildhouse example External precond External effects

20. POP+HTN example a’

21. POP+HTN example a’ d

24. The Gift of magi

27. Sensorless planning

30. Example, the vacuum-world

33. Game tree State node chance node

35. And-Or-search algorithm function AND-OR-GRAPH-SEARCH( problem ) returns a conditional plan or failure return OR-SEARCH(INITIAL-STATE[ problem ], problem , []) function AND-SEARCH( state_set, problem, path ) returns a conditional plan or failure for each s i in state _ set do plan i  OR-SEARCH( s i , problem,path ) if plan = failure then return failure return [ if s 1 then plan 1 else if s 2 then plan 2 else … if s n-1 then plan n-1 else plan n ] function OR-SEARCH( state, problem, path ) returns a conditional plan or failure if GOAL-TEST[ problem ]( state ) then return the empty plan if state is on path then return failure for action,state_set in SUCCESSORS[ problem ]( state ) do plan  AND-SEARCH( state_set, problem, [ state | plan ] ) if plan  failure then return [ action | plan ] return failure

39. PO: alternate double murphy

46. Repair example