HTN-MAKER: Learning HTNs with Minimal Additional Knowledge Engineering Required Chad Hogg 1 Héctor Muñoz-Avila 1 Ugur Kuter 2 1 Department of Computer Science & Engineering Lehigh University Bethlehem, Pennsylvania 18015, USA 2 University of Maryland Institute for Advanced Computer Studies College Park, Maryland 20742, USA 23rd International AAAI Conference on Artificial Intelligence 2008-07-16 C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Motivation Planning systems require some planning knowledge Simple STRIPS planners need only operators, which describe how actions change the world More advanced planners require operators and additional planning knowledge for problem solving Problem Developing additional planning knowledge for complex planners is a difficult manual process performed by domain experts C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Motivation Planning systems require some planning knowledge Simple STRIPS planners need only operators, which describe how actions change the world More advanced planners require operators and additional planning knowledge for problem solving Problem Developing additional planning knowledge for complex planners is a difficult manual process performed by domain experts C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Motivation Planning systems require some planning knowledge Simple STRIPS planners need only operators, which describe how actions change the world More advanced planners require operators and additional planning knowledge for problem solving Problem Developing additional planning knowledge for complex planners is a difficult manual process performed by domain experts C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Motivation (2) Objective We describe an algorithm for learning such planning knowledge for a certain class of advanced planners known as Hierarchical Task Network (HTN) planners Actually, a specific form of HTN planning known as Ordered Task Decomposition (OTD) OTD planners are useful because of potential for efficiency and expressivity; used in most fielded applications C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Motivation (2) Objective We describe an algorithm for learning such planning knowledge for a certain class of advanced planners known as Hierarchical Task Network (HTN) planners Actually, a specific form of HTN planning known as Ordered Task Decomposition (OTD) OTD planners are useful because of potential for efficiency and expressivity; used in most fielded applications C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Motivation (2) Objective We describe an algorithm for learning such planning knowledge for a certain class of advanced planners known as Hierarchical Task Network (HTN) planners Actually, a specific form of HTN planning known as Ordered Task Decomposition (OTD) OTD planners are useful because of potential for efficiency and expressivity; used in most fielded applications C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
STRIPS Planning States are sets of logical Example Operator atoms describing the world :operator Operators describe ((!load-truck ?pkg actions, when they may be ?trk undertaken, and how they ?loc) affect the world :preconditions ((at ?pkg ?loc) The objective is to find a sequence of actions (a (at ?trk ?loc)) :add-effects plan ) that, when executed ((at ?pkg ?loc)) from a given initial state, :del-effects will result in a state that ((in ?pkg ?trk))) satisfies certain given goals C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
STRIPS Planning States are sets of logical Example Operator atoms describing the world :operator Operators describe ((!load-truck ?pkg actions, when they may be ?trk undertaken, and how they ?loc) affect the world :preconditions ((at ?pkg ?loc) The objective is to find a sequence of actions (a (at ?trk ?loc)) :add-effects plan ) that, when executed ((at ?pkg ?loc)) from a given initial state, :del-effects will result in a state that ((in ?pkg ?trk))) satisfies certain given goals C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
STRIPS Planning States are sets of logical Example Operator atoms describing the world :operator Operators describe ((!load-truck ?pkg actions, when they may be ?trk undertaken, and how they ?loc) affect the world :preconditions ((at ?pkg ?loc) The objective is to find a sequence of actions (a (at ?trk ?loc)) :add-effects plan ) that, when executed ((at ?pkg ?loc)) from a given initial state, :del-effects will result in a state that ((in ?pkg ?trk))) satisfies certain given goals C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
STRIPS Planning States are sets of logical Example Operator atoms describing the world :operator Operators describe ((!load-truck ?pkg actions, when they may be ?trk undertaken, and how they ?loc) affect the world :preconditions ((at ?pkg ?loc) The objective is to find a sequence of actions (a (at ?trk ?loc)) :add-effects plan ) that, when executed ((at ?pkg ?loc)) from a given initial state, :del-effects will result in a state that ((in ?pkg ?trk))) satisfies certain given goals C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
HTN Planning Tasks are symbolic Example Method representations of :method activities that should be ((deliver ?pkg accomplished ?loc) Methods describe how to :preconditions decompose complex tasks ((at ?pkg ?src) into sequences of simpler (at ?trk ?src) ones (same-city ?src ?loc)) The objective is to find a :subtasks hierarchy of ((!load-truck ?pkg decompositions from a ?trk given sequence of tasks ?src) down to a sequence of (deliver ?pkg actions that can be ?loc))) executed from the given initial state C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
HTN Planning Tasks are symbolic Example Method representations of :method activities that should be ((deliver ?pkg accomplished ?loc) Methods describe how to :preconditions decompose complex tasks ((at ?pkg ?src) into sequences of simpler (at ?trk ?src) ones (same-city ?src ?loc)) The objective is to find a :subtasks hierarchy of ((!load-truck ?pkg decompositions from a ?trk given sequence of tasks ?src) down to a sequence of (deliver ?pkg actions that can be ?loc))) executed from the given initial state C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
HTN Planning Tasks are symbolic Example Method representations of :method activities that should be ((deliver ?pkg accomplished ?loc) Methods describe how to :preconditions decompose complex tasks ((at ?pkg ?src) into sequences of simpler (at ?trk ?src) ones (same-city ?src ?loc)) The objective is to find a :subtasks hierarchy of ((!load-truck ?pkg decompositions from a ?trk given sequence of tasks ?src) down to a sequence of (deliver ?pkg actions that can be ?loc))) executed from the given initial state C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
HTN Planning Tasks are symbolic Example Method representations of :method activities that should be ((deliver ?pkg accomplished ?loc) Methods describe how to :preconditions decompose complex tasks ((at ?pkg ?src) into sequences of simpler (at ?trk ?src) ones (same-city ?src ?loc)) The objective is to find a :subtasks hierarchy of ((!load-truck ?pkg decompositions from a ?trk given sequence of tasks ?src) down to a sequence of (deliver ?pkg actions that can be ?loc))) executed from the given initial state C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Learning HTN Methods from STRIPS Plans Annotated Tasks We define the notion of an annotated task as a task with effects (what it means to accomplish the task) and preconditions (when it is possible to accomplish the task) Example: (deliver ?pkg ?loc) has effect (at ?pkg ?loc) Given A STRIPS domain D , a collection of annotated tasks T , a planning problem P in D , and a solution plan S to P , Find Method definitions for the tasks in T whose subtasks are actions from S or tasks from T C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Learning HTN Methods from STRIPS Plans Annotated Tasks We define the notion of an annotated task as a task with effects (what it means to accomplish the task) and preconditions (when it is possible to accomplish the task) Example: (deliver ?pkg ?loc) has effect (at ?pkg ?loc) Given A STRIPS domain D , a collection of annotated tasks T , a planning problem P in D , and a solution plan S to P , Find Method definitions for the tasks in T whose subtasks are actions from S or tasks from T C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Learning HTN Methods from STRIPS Plans Annotated Tasks We define the notion of an annotated task as a task with effects (what it means to accomplish the task) and preconditions (when it is possible to accomplish the task) Example: (deliver ?pkg ?loc) has effect (at ?pkg ?loc) Given A STRIPS domain D , a collection of annotated tasks T , a planning problem P in D , and a solution plan S to P , Find Method definitions for the tasks in T whose subtasks are actions from S or tasks from T C. Hogg, H. Muñoz-Avila, U. Kuter HTN-MAKER
Recommend
More recommend
