74.419 artificial intelligence 2005/06 hierarchical planning and other stuff russell and norvig,...
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74.419 Artificial Intelligence 2005/06
Hierarchical Planning
and Other StuffRussell and Norvig, Chapter 11
STRIPS – Plan Schemata
Concrete plans can also be seen as instantiations of Plan Schemata.
Shakey was able to generalize generated concrete plans into such plan schemata.
( plan generalization, learning)
Plan Learning / Plan Abstraction
Shakey generated plan schemata, so-called MACROPs (Macro-Operators), from concrete plans it had constructed earlier (i.e. “it learns plans”).
The learning process is based on describing the plan in a triangle-table (preconditions and effects of sequential actions in rows, actions in columns). Then substitute constants with variables, in a kind of inverse variable binding process with unification. (substitute the same constant with one variable)
Thus, an abstract plan schema is generated from the concrete plan.
Triangle Table
Generate Plan with STRIPS Set up Triangle Table:
left of action: precondition below action: effects (add-list) - record only
literals needed by subsequent actions or as part of the goal clause.
Generalization (abstraction) Substitute constants with variables.
"effect" of start-action initial state
precond. of move(A,B,Fl)
effect of move(A,B,Fl)
precond. of move(B,C,Fl)
precond. of finish
effect of move(B,C,Fl)
Generate Plan Schema
Generalization (abstraction) of concrete plan.
Substitute constants with variables: Fl remains Fl A x B y C z
Abstract Planning
ABSTRIPS
Consider different criticality values of preconditions in planning.
Start with global, abstract plan.
Then refine plan by trying to fulfill preconditions of abstract plan:
• Choose preconditions with highest criticality values first ( = most difficult to achieve).
• Then lower criticality value and continue with planning.
Hierarchical Planning
Principle hierarchical organization of 'actions' complex and less complex (or: abstract) actions lowest level reflects directly executable actions
Procedure planning starts with complex action on top plan constructed through action decomposition substitute complex action with plan of less complex
actions (pre-defined plan schemata; or learning of plans/plan abstraction)
overall plan must generate effect of complex action
Hierarchical Planning
Hierarchical Planning / Plan Decomposition
Plans are organized in a hierarchy. Links between nodes at different levels in the hierarchy denote a decomposition of a “complex action” into more primitive actions (operator expansion).
Example:move (x, y, z)
operatorexpansion pickup (x, y) putdown (x, z)
The lowest level corresponds to executable actions of the agent.
Hierarchical Plan - Example
Travel (source, dest.)
Take-Plane Take-Bus Take-Car
Goto (bus, source) Buy-Ticket (bus) Hop-on (bus) Leave (bus, dest.)
Goto (counter) Request (ticket) Pay (ticket)
Extensions and Modificationsto Basic Planning Methods
ADL - Action Definition Language
ADL Can be seen as extension of the STRIPS language.Contains typing of parameters (sorts).Allows explicit expression of negation.Allows equality of terms in precondition formula.
Example:
Fly (p: plane; from: airport; to: airport; c: cargo)precondition: at(p,to) at(c,to) in(c,p) tofromeffect: at(p,to) at(c,to) at(p,from) at(c,from)
From Russell & Norvig, Chapter 11
Resource Constraints in Planning Resources
physical quantities, e.g. money, fluids etc. time
Integrate Measures into Action Description and Planning representation of physical quantities and reasoning /
calculation, e.g. for buy-action: effect: cash := cash – price (x) time system / time logic, e.g. go-to-action: effect: time := time + 30 (Minutes)
Backtracking on Constraint Violation
Other Issues in Planning
Disjunctive Preconditions planning with alternatives
Disjunctive Effects parallel future worlds to consider
All-Quantified Variables (in preconditions and effects) only for finite, static Universe of objects
Conditional Planning action depends on conditions specified concretely only at plan execution time typically based on percepts/sensor information integrate into partial order planning with threats
Real World Agents 1 Consider Sensors and Effectors
perception of environment, e.g. vision ensure correspondence between internal map of
robot and environment, e.g. locating robot low-level body control, e.g. Motion Control (behaviour
routines, e.g. Fuzzy or Neural Network Controller) other sensor information for body control and
environment mapping, e.g. bumpers, radar sensors for other information channels and cognitive
processes, e.g. speech – language
Real World Agents 2 Low-level Processing and Control
Motion Control Audio Recording and low-level analysis
Medium-level Processing Navigation / Route Planning Robot Location
Higher-level Processing Speech Recognition, NLP, ... Strategies, Planning BDI (Belief-Desire-Intention) - Architecture
Real World Agents 3
Multi-Agents Language / Communication
communicating agents Mental Models of other Agents
cooperating agents Strategies
cooperating agents Deontic Systems Trust
Additional References
Nils J. Nilsson: Artificial Intelligence – A New Synthesis. Morgan Kaufmann, San Francisco, 1998.
Konolidge, K. and K. Myers: The Saphira Architecture for Autonomous Mobile Robots (Robot Soccer Class Project)
Guzzoni, D. et al.: Many Robots Make Short Work. (AAAI’96 Robot Competition - Meeting Scheduling)
Martina Veloso, MIT (RoboCup)
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