Hierarchical Task Network Planning Automated Planning: Theory and Practice, Chapter 11 소프트컴퓨팅 연구실

Hierarchical Task Network Planning Motivation –To construct plans from individual operators is a waste of time –Many task in real life already have a built-in hierarchical structure –How to enable planning systems to make use of such recipes? HTN (hierarchical task network) planning –HTN is suitable for domains where tasks are naturally organized in a hierarchy –Hierarchical decomposition: Use abstract operators as well as primitive operators during plan generation –Use abstract operators to start a plan –Use partial-order planning techniques and action decomposition to come up with the final plan –The final plan contains only primitive operators

Simple Task Network (STN) Planning A Special case of HTN planning Definitions –γ (s, a) : the result of applying an action to a state s –STN: w = (U, E), U is the node set, E is the edge set u ∈ U contains a task t u –Task: an expression of the form t(u 1, …, u n ) t is a task symbol, and each u i is a term Two kinds of task symbols (and tasks): –Primitive: tasks that we know how to execute directly –Nonprimitive: tasks that must be decomposed into subtasks –STN method: m = (name(m), task(m), precond(m), network(m) or subtasks(m)) name(m): an expression of the form n(x 1,…,x n ) x 1,…,x n : parameters – variable symbols –task(m): a nonprimitive task –precond(m): preconditions –subtasks(m): a sequence of tasks (total order) –network(m): ({u 1,…,u k },{(u 1,u 2 ),…(u k-1,…,u k )}) (partial order)

Formal Descriptions of the Methods for DWR

STN Planning Problem and Solutions STN planning domain –D = (O, M) –O: a set of operators –M: a set of methods STN planning problem –P = (s 0, w, O, M) –s 0 : the initial state –w: a task network called the initial task network –D = (O, M): an STN planning domain

STN Planning Problem and Solutions

Solving Total-Order STN Planning Problems

Decomposition Tree for move-stack Method

Solving Partial-Order STN Planning Problems

TFD vs. PFD

TFD Solution

PFD Solution

HTN Planning HTN task network –w = (U, C), U is a set of task nodes, C is a set of constraints –A precedence constraint: the edge in STN planning –A before-constraint: a generalization of the notion of a precondition in STN planning –An after-constraint: after(U’, l), l must true just after last (U’, π) –A between-constraint: between(U’, U’’, l), l must true in the state just after last (U’, π), the state just before first (U’’, π), and all of the states in between HTN Method is a 4-tuple –m = (name(m), task(m), subtasks(m), constr(m)) –task(m): a nonprimitive task –(subtasks(m), constr(m)): a task network

Examples of HTN Methods

HTN Planning Problem and Solutions STN planning domain –D = (O, M) –O: a set of operators –M: a set of methods STN planning problem –P = (s 0, w, O, M) –s 0 : the initial state –w: a task network called the initial task network –D = (O, M): an STN planning domain

HTN Planning Problem and Solutions

Abstract-HTN procedure

Comparisons Comparison to classical planning –STN and HTN can be used to encode undecidable problems –STN and HTN planning is strictly more expressive than classical planning A classical planning problem is a regular language A total-order STN planning problem is a context-free language HTN methods versus control rules –Control-rule planning is restricted to solving just classical planning problems –HTN planning has more expressive power than control-rule planning –By extensions, both formalisms are capable of representing undecidable problems –Hard to say which type of control knowledge is more effective

Discussion HTN 과 행동 네트워크와의 차이점은 무엇일까 ? Theory of planned behavior (TPB) – 행동과 행동에 대한 사용자의 의도를 표현한 심리학적 모델 S1 S2 S3 S4 S5 S6 S7 Goal1 2 Behavior 입력영상 … 서비스 1 2 로그및컨텍스트 서비스 1 2 환경 입력 1 2 서비스 행동 네트워크

Discussion TPB+HTN –Sequential behaviors for one task – Task intention & perceived control task TPB+BeN – 각 Behavior 에 TPB 모형을 결합 S1 S2 S3 S4 S5 S6 S7 Goal1 2 Behavior 입력영상 … 서비스 1 2 로그및컨텍스트 서비스 1 2 환경 입력 1 2 서비스