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CS 63. Practical Planning: Scheduling and Hierarchical Task Networks. Chapter 12.1-12.2. Adapted from slides by Tim Finin and Marie desJardins. Outline. Intelligent scheduling Hierarchical task network (HTN) planning Increasing expressivity. Real-world planning domains.

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practical planning scheduling and hierarchical task networks

CS 63

Practical Planning:Scheduling and Hierarchical Task Networks

Chapter 12.1-12.2

Adapted from slides by

Tim Finin and

Marie desJardins.

outline
Outline
  • Intelligent scheduling
  • Hierarchical task network (HTN) planning
  • Increasing expressivity
real world planning domains
Real-world planning domains
  • Real-world domains are complex and don’t satisfy the assumptions of STRIPS or partial-order planning methods
  • Some of the characteristics we may need to deal with:
    • Modeling and reasoning about resources
    • Representing and reasoning about time
    • Planning at different levels of abstractions
    • Conditional outcomes of actions
    • Uncertain outcomes of actions
    • Exogenous events
    • Incremental plan development
    • Dynamic real-time replanning

}a.k.a. scheduling!

planning vs scheduling
Planning vs. scheduling
  • Planning: given one or more goals, generate a sequence of actions to achieve the goal(s)
  • Scheduling: given a set of actions and constraints, allocate resources and assign times to the actions so that no constraints are violated
  • Traditionally, planning is done with specialized logical reasoning methods
  • Traditionally, scheduling is done with constraint satisfaction, linear programming, or OR methods
  • However, planning and scheduling are closely interrelated and can’t always be separated
hierarchical decomposition
Hierarchical decomposition
  • Hierarchical decomposition, or hierarchical task network (HTN) planning, uses abstract operators to incrementally decompose a planning problem from a high-level goal statement to a primitive plan network
  • Primitive operators represent actions that are executable, and can appear in the final plan
  • Non-primitive operators represent goals (equivalently, abstract actions) that require further decomposition (or operationalization) to be executed
  • There is no “right” set of primitive actions: One agent’s goals are another agent’s actions!
htn operator example
HTN operator: Example

OPERATOR decompose

PURPOSE: Construction

CONSTRAINTS:

Length (Frame) <= Length (Foundation),

Strength (Foundation) > Wt(Frame) + Wt(Roof)

+ Wt(Walls) + Wt(Interior) + Wt(Contents)

PLOT: Build (Foundation)

Build (Frame)

PARALLEL

Build (Roof)

Build (Walls)

END PARALLEL

Build (Interior)

sipe 2
SIPE-2
  • SIPE-2 is an HTN planner with many advanced features:
    • Plan critics
    • Resource reasoning
    • Constraint reasoning (complex numerical or symbolic variable and state constraints)
    • Interleaved planning and execution
    • Interactive plan development
    • Sophisticated truth criterion
    • Conditional effects
    • Parallel interactions in partially ordered plans
    • Replanning if failures occur during execution
sipe 29
SIPE-2

Image from: http://www.ai.sri.com/~sipe/architecture.html

blocksworld in sipe 2
Blocksworld in SIPE-2

Excerpt from SIPE-2

Blocksworld Definition

Sussman Anomaly

;;some colored blocks for other problems

(ON R1 B1)

(ON B1 TABLE)

(ON B2 TABLE)

(ON R2 TABLE)

;true in all problems

(CLEAR TABLE)

END PREDICATES

STOP

OPERATOR: PUTON1

ARGUMENTS: BLOCK1, OBJECT1 IS NOT BLOCK1;

PURPOSE: (ON BLOCK1 OBJECT1)

PLOT:

PARALLEL

BRANCH 1:

GOALS: (CLEAR OBJECT1)

BRANCH 2:

GOALS: (CLEAR BLOCK1)

END PARALLEL

PROCESS

ACTION: PUTON;

ARGUMENTS: BLOCK1,OBJECT1

RESOURCES: BLOCK1

EFFECTS: (ON BLOCK1 OBJECT1)

END PLOT

END OPERATOR

Excerpt taken from http://www.ai.sri.com/~sipe/blocks-sipe.txt

Image taken from http://www.ai.sri.com/~sipe/sussman-derivation.html

htn operator representation
HTN operator representation
  • Russell & Norvig explicitly represent causal links; these can also be computed dynamically by using a model of preconditions and effects (this is what SIPE-2 does)
  • Dynamically computing causal links means that actions from one operator can safely be interleaved with other operators, and subactions can safely be removed or replaced during plan repair
  • Russell & Norvig’s representation only includes variable bindings, but more generally we can introduce a wide array of variable constraints
truth criterion
Truth criterion
  • Determining whether a formula is true at a particular point in a partially ordered plan is, in the general case, NP-hard
  • Intuition: there are exponentially many ways to linearize a partially ordered plan
  • In the worst case, if there are N actions unordered with respect to each other, there are N! linearizations
  • Ensuring soundness of the truth criterion requires checking the formula under all possible linearizations
  • Use heuristic methods instead to make planning feasible
  • Check later to be sure no constraints have been violated
truth criterion in sipe 2
Truth criterion in SIPE-2
  • Heuristic: prove that there is one possible ordering of the actions that makes the formula true – but don’t insert ordering links to enforce that order
  • Such a proof is efficient
    • Suppose you have an action A1 with a precondition P
    • Find an action A2 that achieves P (A2 could be initial world state)
    • Make sure there is no action necessarily between A2 and A1 that negates P
  • Applying this heuristic for all preconditions in the plan can result in infeasible plans
increasing expressivity
Increasing expressivity
  • Conditional effects
    • Instead of having different operators for different conditions, use a single operator with conditional effects
    • Move (block1, from, to) and MoveToTable (block1, from) collapse into one Move (block1, from, to):
      • Op(ACTION: Move(block1, from, to),PRECOND: On (block1, from) ^ Clear (block1) ^ Clear (to)EFFECT: On (block1, to) ^ Clear (from) ^ ~On(block1, from) ^ ~Clear(to) when to≠Table
      • There’s a problem with this operator: can you spot what it is?
  • Negated and disjunctive goals
  • Universally quantified preconditions and effects
reasoning about resources
Reasoning about resources
  • Introduce numeric variables that can be used as measures
  • These variables represent resource quantities, and change over the course of the plan
  • Certain actions may produce (increase the quantity of) resources
  • Other actions may consume (decrease the quantity of) resources
  • More generally, may want different types of resources
    • Continuous vs. discrete
    • Sharable vs. nonsharable
    • Reusable vs. consumable vs. self-replenishing
other real world planning issues
Other real-world planning issues
  • Conditional planning
  • Partial observability
  • Information gathering actions
  • Execution monitoring and replanning
  • Continuous planning
  • Multi-agent (cooperative or adversarial) planning
planning summary
Planning summary
  • Planning representations
    • Situation calculus
    • STRIPS representation: Preconditions and effects
  • Planning approaches
    • State-space search (STRIPS, forward chaining, ….)
    • Plan-space search (partial-order planning, HTN, …)
    • Constraint-based search (GraphPlan, SATplan, …)
  • Search strategies
    • Forward planning
    • Goal regression
    • Backward planning
    • Least-commitment
    • Nonlinear planning
applications of planning
Applications of Planning
  • Military operations
  • Autonomous space operations
  • Construction tasks
  • Machining tasks
  • Mechanical assembly
  • Design of experiments in genetics
  • Command sequences for satellite

Most applied systems use extended

representation languages, nonlinear

planning techniques, and domain-specificheuristics

oil spill response in sipe 2
Oil-Spill Response in SIPE-2

Image taken from http://www.ai.sri.com/~sipe/oil.html

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