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```74.419 Artificial
Intelligence 2005/06
Partially Ordered Plans - or:
"How Do You Put Your Shoes On?"
Partially Ordered Plans
Partially Ordered Plans - or:
"How Do You Put Your Shoes On?"
Partially Ordered Plans:
• no strict sequence
• partly parallel
• observe threats
Least Commitment Strategy
Partially Instantiated Plans
Least Commitment Strategy
In general, make as little concrete as possible,
i.e. leave things undetermined until you have to
determine them and become concrete.
Partially Instantiated Plans
During planning, variables have not necessarily
to be instantiated immediately.
Instantiation can wait, until binding becomes
necessary
Partial Order Planning 1
First plan consists only of start and finish actions:
• start - T as precondition, initial world state as
effect
• finish - goal as precondition, NIL as effect
Select actions to achieve sub-goals separately, quasi
in parallel  partial-order plan
Fulfill open preconditions (sub-goals), until no more
unsatisfied preconditions are left (last one is T of
start)
Partial Order Planning 2
matching preconditions for plan.
Causal links specify a partial order.
Recognize threats - the effect of an action A
negates the precondition of another action B.
Add threats as partial order to plan: B<A (do B
before A).
Partial Order Planning - Threats
partial order plan = set of action strings (partial plans)
Problem:
Detect and resolve threats, i.e. conflicts between actions
– where the precondition of one action is deleted by
another action – by choosing an adequate ordering of
actions: if action b is a threat to action a, then a<b, i.e. a
has to occur before b.
Partial Order Planning - Overall
Use plan transformation operators to refine the partial plan
and construct a complete plan:
• reorder actions (operators),
• instantiate actions (operators).
A partial order plan consists of a set of action sequences
(partial plans; action strings) which together achieve the
complete set of goal literals.
Threats induce an additional partial order of these action
sequences.
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)
operator
expansion
pickup (x, y)
putdown (x, z)
The lowest level corresponds to executable actions of the
agent.
Hierarchical Planning
Hierarchical Planning / Plan Decomposition
• hierarchical organisation of 'actions'
• complex and less complex (or: abstract) actions
• lowest level reflects directly executable actions
• 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, cf. ABSTRIPS)
• overall plan must generate effect of complex action
Abstract Planning
ABSTRIPS
Consider different criticality values of preconditions
in planning.
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.
Other Issues in Planning
Disjunctive Preconditions
Conditional Effects
 change is due to specific condition
 integrate into partial planning with threats
Disjunctive Effects
 parallel future worlds to consider
All-Quantified Variables (in preconditions and effects)

only for finite, static Universe of objects
Real World Agents 1

Consider Sensors and Effectors
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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
sensors for other information channels and cognitive
processes, e.g. speech – language
Real World Agents 2

Low-level Processing and Control
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Motion Control
Audio Recording and low-level analysis
Medium-level Processing

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Robot Location
Higher-level Processing
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Speech Recognition, NLP, ...
Strategies, Planning
BDI (Belief-Desire-Intention) - Architecture
Real World Agents 3

Multi-Agents
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Language / Communication →
communicating agents
mental Models of other Agents
cooperating agents
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Strategies
cooperating agents
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Deontic Systems
Trust
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)