planning based on model checking dept. of information systems and applied cs bamberg university...

Planning based on Model Checking

Dept. of Information Systems and Applied CS Bamberg University

Seminar Paper

Svetlana Balinova

1. Introduction

2. Explicit Model Checking

3. Temporal Logic

4. Symbolic Model Checking

5. Binary Decision Diagrams

6. Planning for Reachability Goals

Outline

„Model Checking is an automatic technique for

verifying correctness properties

of safety-critical reactive systems “

1. Introduction

Classical planning Planning under uncertainty

Determinism Nondeterminsm

Full observability Partial observability

Reachability goals Extended Goals

2 kinds of correctness properties:

Safety

Liveness

State space: a system, implemented as Kripke Structure

Verification: build a computation tree for all possible paths within a

System



green signal

Warning system for planes

signal, evasion recommendation

Properties to be checked must be formalized in a temporal logic.

A temporal logic provides operators which represent time dependences.

Operators: F (in the future), X (next time), G (globaly)

Path quantifiers: A (always), E (exist)

CTL (Computation Tree Logic) – branching time.

LTL (Linear-Time Temporal Logic) – linear time.

Validity of LTL and CTL formulas: Model Checking Algorithms.

3. Temporal Logic

3. Temporal Logic

Examples:

φ φ

φ LTL formula: F φ

CTL formula: AX φ

…………………….

……..

Faces the state explosion problem of explicit state Model Checking.

Exploring sets of states, rather than single states.

In order to represent a model checking problem symbolically, we need

to represent symbolically:


the sets of states of a Kripke Structure,

its transition relation,

and the model checking algorithms.

Symbolic Representation of Sets of States:

A vector x of Boolean variables where each variable corresponds to a

an atomic propostion in P.

x = { green, signal, evasiion recommendation}

A state s is represented with a formula ξ(s) on the propositions:

ξ(so) = green, ¬signal, ¬evasion recommendation

ξ(s1) = ¬green, signal, ¬evasion recommendation

ξ(s2) = ¬green, signal, evasion recommendation


A set of states Q S represented symbolically as:

Symbolic Representation of Transition Relations

A vector of state variables x = <x1, ......, xn> and a further vector of

next state variables x‘ = <x‘1, ......, x‘n>

x‘ = { green’, signal’, evasion recommendation’}

A transition ξ(< so, s1 >) encoded as:

ξ(< so, s1 >) = ξ(< so >) , ξ‘(< s1 >)

ξ(< so, s1 >) = (green, ¬signal, ¬evasion recommendation),

(¬green‘, signal’, ¬evasion recommendation’)

Transition relation R represented symbolically as:

ξ(R) = V ξ(r)

ξ(R) = V ξ(r)


r є R

Symbolic Representation of Model Checking Algorithms

Replace each function call with the symbolic counterpart.

Cast the operations on sets into the corresponding operations on

propositional formulas.


An efficient approach for manipulation of Boolean formualas.

A binary decision diagram represents a Boolean function as rooted,

directed acyclic graph.

Each nonterminal vertex v is labeled by a variable var(v) and has ars

directed toward two children: lo(v) and hi(v). In the first case the

variable is assigned 0(- - -) and in the second 1( ).

Each terminal vertex is labeled 0 or 1

A Boolean function may be represented by a truth table, binary

decision tree etc. A tree is said to be ordered if the variables always

occur in the same order along any path from root to leaf.

5.Binary Decision Diagrams


Truth Table and Decision Tree Representations of a Boolean Function. A dashed (solid) tree branch denotes the case where the decision variable is 0 (1).

Example:

Reduction of decision tree to OBDD

1. Remove Duplicate Terminals: Eliminate all but one terminal vertex with a given label and redirect all arcs into the eliminated vertices to the remaining one.

2. Remove Duplicate Nonterminals: If nonterminal vertices u and v have var(u)=var(v), lo(u)=lo(v), and hi(u)=hi(v), then eliminate one of the two vertices and redirect all incoming arcs to the other vertex.

3. Remove Redundant Tests: If nonterminal vertex v has lo(v)=hi(v), then eliminate v and redirect all incoming arcs to lo(v).



Applying the three reduction rules to the tree of the last example yields the canonical representation of the function as an OBDD.

Rechability goals: Goals are sets of states, i.e., the objective is to

build a plan that leads to one of the goal states.

A planning domain is a nondeterministic state-transition system

Σ= (S, A, ), where:

S is a finite set of states A is a finite set of actions : S A 2s is the state-transition function.



Example: nondeteministic state-transition system for a simplified DWR (dock-worker-robots) domain


A plan is a policy, i.e. a function that maps states into actions.

A policy π for a planning domain Σ= (S, A, ) is a set of pairs (s, a)

such that (s, a) є A(s).

π1 = {(s1, move(r1,l1,l2)), (s2, move(r1,l2,l3)), (s3, move(r1,l3,l4))}

π2 = {(s1, move(r1,l1,l2)), (s2, move(r1,l2,l3)), (s3, move(r1,l3,l4)), (s5, move(r1,l3,l4))}

π3 = {(s1, move(r1,l1,l4))}

Policies for the domain in the previous examle:

We represent the execution of a policy in a planning domain with an

execution structure, i.e., a directed graph in which the nodes are all of

the states of the domain that can be reached by executing actions in

the policy, and the arcs represent possible state transitions caused by

actions in the policy.


π1 π2 π3

A planning problem is a triple (Σ, So, Sg), where Σ= (S, A, ) is a

planning domain, So S is a set of initial states, and Sg S is a set

of goal states.

Types of solutions for a planning problem:

weak solutions

strong solutions

strong cyclic solutions

Planning algorithms – designed to work on sets of states, thus taking

advantage of the BDD-based symbolic Model Checking.


Thank you for your attention!

planning based on model checking dept. of information systems and applied cs bamberg university...

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