TNC Planner

Task Network Compiler (Click here for full screen)

Insert and Run you own problem domain into Facts and Operators below

Default Example 1. RoundTrip Problem. Intermediate goals.

Sinopsis: the user starts from Perugia and wants a plan with an intermediate goal task1 (i.e. be at Pisa) and put him back to Perugia.
The initial state encodes the topology of the road network, and specifies the final goals events and task goals of the user problem.

Give a look to the problem domain below, press the solve button
then try to modify the problem domain, or submit you own domain (see TNC Sintax Instructions , About Examples,
How to run TNC)

TNC Sintax

The syntax for defining facts and operators is based on Graphplan syntax with the following extensions:

(tasks taskslist)
(events eventslist)
(order orderlist)

where taskslist is a list of tasks, each of them defined by the syntax

(label do action)
(label achieve conditions)

eventslist is a list of events, defined by the syntax
(label conditions)
and orderlist is a list of temporal constraints among tasks and events defined by
(label1 label2).

Graphplan syntax
Every object has a type specified as

(object type) Example: (perugia place) (roma place)

Initial state is specified as a special preconds fact, a list of facts which are true:

(preconds fact_sequence ) Example: (preconds (at perugia) (road perugia roma) (road roma perugia) )

Goal state is specified as a special effects fact, a list of facts which are required to be true:

(effects fact_sequence ) Example: (effects (at perugia) (road perugia roma) (road roma perugia) )

Operators are specified by the classical preconditions/effects scheme with typed parameters :

(operator op_name (params paramtypesequence ) (preconds fact_sequence) (effects fact_sequence) )

Example: (operator goto
                (params (<l1> place)(<l2> place))
                (preconds (at <l1>) (route <l1> <l2>))
                (effects (del at <l1>)(at <l2>)) )

TNC Architecture

TNC compiles the extended problem into the domain, it generates an appropriate and equivalent classical problem domain by adding artificial dummy operators, dummy facts and dummy goals.

TNC receives in

input: a task network, i.e. a problem specification which extend the classical state based problem

output: a classical state based domain which is equivalent to the original extended planning problem.

TNC References

Task Planning and Partial Order Planning: A Domain Transformation Approach
Marco Baioletti, Stefano Marcugini, Alfredo Milani
Lecture Notes in AI vol.1348, 52-63, Springer-Verlag 1997
4th European Conference On Planning, Sept 24-26 1997, Toulose, France
Encoding Planning Constraints into Partial Order Planning Domains
Marco Baioletti, Stefano Marcugini, Alfredo Milani
in KR98 Proceedings of 6th International Conference on Principles of Knowledge Representation and Reasoning, Trento, Italy, June 2-5, 1998
Partial Plans Completion with GRAPHPLAN
Marco Baioletti, Stefano Marcugini, Alfredo Milani,
Workshop on Planning as Combinatorial Search, Pittsburgh, USA, June 7, 1998

TNC Sintax

TNC Architecture

TNC References

Visitors since 9 Oct 1998