AAAI-2014 Tutorial A Concise Introduction to Planning

AAAI-2014 Tutorial

A Concise Introduction toPlanning Models and Methods

Hector GeffnerICREA & Universitat Pompeu Fabra

Barcelona, Spain

Blai BonetUniversidad Simon Bolıvar

Caracas, Venezuela

H. Geffner and B. Bonet, Planning Tutorial, AAAI-2014, 7/2014 1

Outline

• Introduction to Planning and Problem Solving

• Classical Planning: Deterministic actions and complete information

• Beyond Classical Planning: Transformations

• Probabilistic Models: Markov Decision Processes (MDPs), and Partially Ob-servable MDPs (POMDPs)

• Challenges. Summary

– Reference: A concise introduction to models and methods for automated planning, H. Geffner

and B. Bonet, Morgan & Claypool, 6/2013.

– Other references: Automated planning: theory and practice, M. Ghallab, D. Nau, P. Traverso.

Morgan Kaufmann, 2004, and Artificial intelligence: A modern approach. 3rd Edition, S. Russell

and P. Norvig, Prentice Hall, 2009.

– Relevant biblio: listed at the end


What is planning?

• Thinking before acting

• Thinking what to do next

• Thinking how best to achieve given goal

• Use predictive model for action selection or control . . .

We’ll make this all precise and address:

• What is planning

• Why planning is hard

• How can (automated) planning be done effectively

• Different types of plans and settings


Planning and Autonomous Behavior

Key problem in autonomous behavior is control: what to do next. Threeapproaches to this problem:

• Programming-based: Specify control by hand

• Learning-based: Learn control from experience

• Model-based: Specify problem by hand, derive control automatically

Approaches not orthogonal though; and successes and limitations in each . . .

Planning is the model-based approach to autonomous behavior; model capturespredictions: what actions do in the world, and what sensors tell us about the world


Wumpus World PEAS description

Performance measuregold +1000, death -1000-1 per step, -10 for using the arrow

EnvironmentSquares adjacent to wumpus are smellySquares adjacent to pit are breezyGlitter iff gold is in the same squareShooting kills wumpus if you are facing itShooting uses up the only arrowGrabbing picks up gold if in same squareReleasing drops the gold in same square

Breeze Breeze

Breeze

BreezeBreeze

Stench

Stench

BreezePIT

PIT

PIT

1 2 3 4

1

2

3

4

START

Gold

Stench

Actuators Left turn, Right turn,Forward, Grab, Release, Shoot

Sensors Breeze, Glitter, Smell

Chapter 7 5H. Geffner and B. Bonet, Planning Tutorial, AAAI-2014, 7/2014 5

Planners, Models and Solvers

Problem =⇒ Solver =⇒ Solution

• It’s also useful to see planners in relation to other AI models and solvers:

. Constraint Satisfaction/SAT: find state that satisfies constraints

. Bayesian Networks: find probability over variable given observations

. Planning: find action sequence or policy that produces desired state

. Answer Set Programming: find answer set of logic program

. General Game Playing: find best strategy in presence of n-actors, ...

• Solvers for these models are general; not tailored to specific instances

• Models are all intractable, and some extremely powerful (POMDPs)

• Solvers all have a clear and crisp scope; they are not architectures

• Challenge is mainly computational: how to scale up

• Methodology is empirical: benchmarks and competitions

• Significant progress . . .


Familiar Model and Solver: Linear Equations

Problem =⇒ Solver =⇒ Solution

• Problem: The age of John is 3 times the age of Peter. In 10 years, it will beonly 2 times. How old are John and Peter?

• Expressed as: J = 3P ; J + 10 = 2(P + 10)

• Solver: Gauss-Jordan (Variable Elimination)

• Solution: P = 10 ; J = 30

Solver is general as deals with any problem expressed as an instance of model

Linear Equations Model, however, is tractable; AI models are not

For AI solvers to scale up, structure of problems needs to be exploited


SAT

• SAT is the problem of determining whether there is a truth assignment thatsatisfies a set of clauses

x ∨ ¬y ∨ z ∨ ¬w ∨ · · ·

• Problem is NP-Complete, which in practice means worst-case behavior of SATalgorithms is exponential in number of variables (2100 = 1030)

• Yet current SAT solvers manage to solve problems with thousands of variablesand clauses, and used widely (circuit design, verification, planning, etc)


How SAT solvers manage to do it?

Two types of efficient (poly-time) inference in every node of the search tree:

• Unit Resolution:

. Derive clause C from C ∨ L and unit clause ∼L

• Conflict-based Learning and Backtracking:

. When empty clause 2 derived, find ’causes’ S of 2, add ¬S to theory, andbacktrack til S disabled

Other ideas are logically possible but do not work (do not scale up):

• Generate and test each one of the possible assignments (pure search)

• Apply resolution without the unit restriction (pure inference)


Basic (Classical) Planning Model and Task

• A system that can be in one of many states

• States assign values to a set of variables

• Actions change the values of certain variables

• Basic task: find action sequence to drive initial state into goal state

Model =⇒ Box =⇒ Action sequence

• Complexity: NP-hard; i.e., exponential in number of vars in worst case

• Box is generic; it should work on any domain no matter what variables are about


Concrete Planning Example

B CA

CAB A B C

BAC

BCA

ABC

CA B A

BC

CBA

A B CABC

BCA

......... ........

GOAL

GOALINIT

..........

....

....

• Given the actions that move a ’clear’ block to the table or onto another ’clear’block, find a plan to achieve the goal

• How to find path in the graph whose size is exponential in number of blocks?


How Problem Solved? Heuristics Derived Automatically

B CA

CAB A B C

BAC

BCA

ABC

CA B A

BC

CBA

A B CABC

BCA

......... ........

GOAL

h=3

h=3 h=2 h=3

h=1

h=0

h=2 h=2 h=2

h=2

GOALINIT

• Heuristic evaluations h(s) provide ‘sense-of-direction’

• Derived efficiently in a domain-independent fashion from relaxations whereeffects made monotonic (delete relaxation).


Models, Solvers, and Inference: A bit of Cognitive Science

• We have learned a lot about effective inference mechanisms in last 20–30years from work on scalable domain-independent solvers

• The problem of AI in the 80s with knowledge-based approach was not just lackof (commonsense) knowledge, but lack of effective inference mechanisms

• Commonsense based not only on massive amounts of knowledge, but also massiveamounts of fast and effective but unconscious inference

• This is evident in Vision and NLP but no less true in Everyday Reasoning

• The unconscious, not necessarily Freudian, getting renewed attention:

. Strangers to Ourselves: the Adaptive Unconscious by T.Wilson (2004)

. The New Unconscious, by Ran R. Hassin et al. (Editors) (2004)

. Blink: The Power Of Thinking Without Thinking by M. Gladwell (2005)

. Gut Feelings: The Intelligence of the Unconscious by Gerd Gigerenzer (2007)

. . . .

. Thinking, Fast and Slow. D. Kahneman (2011)


Planning Models: Classical AI Planning

• finite and discrete state space S

• a known initial state s0 ∈ S

• a set SG ⊆ S of goal states

• actions A(s) ⊆ A applicable in each s ∈ S

• a deterministic transition function s′ = f(a, s) for a ∈ A(s)

• positive action costs c(a, s)

A solution or plan is a sequence of applicable actions π = a0, . . . , an that maps s0into SG; i.e., there are states s0, . . . , sn+1 s.t. si+1 = f(ai, si) and ai ∈ A(si) fori = 0, . . . , n, and sn+1 ∈ SG.

The plan is optimal if it minimizes the sum of action costs∑i=0,n c(ai, si). If

costs are all 1, plan cost is plan length

Different models obtained by relaxing assumptions in bold . . .


Uncertainty but No Feedback: Conformant Planning

• finite and discrete state space S

• a set of possible initial state S0 ∈ S

• a set SG ⊆ S of goal states

• actions A(s) ⊆ A applicable in each s ∈ S

• a non-deterministic transition function F (a, s) ⊆ S for a ∈ A(s)

• uniform action costs c(a, s)

A solution is still an action sequence but must achieve the goal for any possibleinitial state and transition

More complex than classical planning, verifying that a plan is conformant in-tractable in the worst case; but special case of planning with partial observability


Planning with Markov Decision Processes

MDPs are fully observable, probabilistic state models:

• a state space S

• initial state s0 ∈ S

• a set G ⊆ S of goal states

• actions A(s) ⊆ A applicable in each state s ∈ S

• transition probabilities Pa(s′|s) for s ∈ S and a ∈ A(s)

• action costs c(a, s) > 0

– Solutions are functions (policies) mapping states into actions

– Optimal solutions minimize expected cost to goal


Partially Observable MDPs (POMDPs)

POMDPs are partially observable, probabilistic state models:

• states s ∈ S

• actions A(s) ⊆ A


• observable goal states SG

• initial belief state b0

• sensor model given by probabilities Pa(o|s), o ∈ Obs

– Belief states are probability distributions over S

– Solutions are policies that map belief states into actions

– Optimal policies minimize expected cost to go from b0 to goal


Example

Agent A must reach G, moving one cell at a time in known map

A

G

• If actions deterministic and initial location known, planning problem is Classical

• If actions non-deterministic and location observable, it’s an MDP or FOND

• If actions non-deterministic and location partially obs, POMDP or Contingent

Different combinations of uncertainty and feedback: diff problems, diff models

Planner is generic solver for instances of a particular model

Classical planners, MDP Planners, POMMDP planners, . . .


Models, Languages, and Solvers

• A planner is a solver over a class of models; it takes a model description, andcomputes the corresponding controller

Model Instance =⇒ Planner =⇒ Controller

• Many models, many solution forms: uncertainty, feedback, costs, . . .

• Models described in compact form by means of planning languages (Strips,PDDL, PPDDL, . . . ) where states represent interpretations over the language.


Language for Classical Planning: Strips

• A problem in Strips is a tuple P = 〈F,O, I,G〉:

. F stands for set of all atoms (boolean vars)

. O stands for set of all operators (actions)

. I ⊆ F stands for initial situation

. G ⊆ F stands for goal situation

• Operators o ∈ O represented by

. the Add list Add(o) ⊆ F

. the Delete list Del(o) ⊆ F

. the Precondition list Pre(o) ⊆ F


From Language to Models

A Strips problem P = 〈F,O, I,G〉 determines state model S(P ) where

• the states s ∈ S are collections of atoms from F (valuations over F )

• the initial state s0 is I (initially an atom in F is true iff it’s in I)

• the goal states s are such that G ⊆ s (. . . )

• the actions a in A(s) are ops in O s.t. Prec(a) ⊆ s

• the next state is s′ = s \Del(a) ∪Add(a)

• action costs c(a, s) are all 1

– (Optimal) Solution of P is (optimal) solution of S(P )

– Slight language extensions often convenient (e.g., negation and conditionaleffects); some required for describing richer models (costs, probabilities, ...).


Example: Simple Problem in Strips

Problem P = 〈F, I,O,G〉 where:

• F = {p, q, r}

• I = {p}

• G = {q, r}

• O has two actions a and b such that:

. Prec(a) = {p} , Add(a) = {q}, Del(a) = {}

. Prec(b) = {q} , Add(b) = {r}, Del(b) = {q}


More meaningful example: Carrying packages

Carrying packages between rooms; P = 〈F, I, O,G〉 where p2ATrA is pkg A at room A, etc.

• F = {p1ATrA, p1ATrB, p2ATrA, p2ATrB, robotATrA, robotATrB, p1held, p2held}• I = {p1ATrA, p2ATrA, robotATrB}• G = {p1ATrB, p2ATrB}• O contains 10 actions:

. Pick-p1rA: Prec = {p1ATrA, robotATA}, Add = {p1held}, Del = {p1ATrA}

. Pick-p1rB: Prec = {p1ATrB, robotATB}, Add = {p1held}, Del = {p1ATrB}

. Drop-p1rA: Prec = {p1held, robotATA}, Add = {p1ATrA}, Del = {p1held}

. Drop-p1rB: Prec = {p1held, robotATB}, Add = {p1ATrB}, Del = {p1held}

. Move-A-to-B: Prec = {robotATA}, Add = {robotATB}, Del = {robotATA}

. Move-B-to-A: Prec = {robotATB}, Add = {robotATA}, Del = {robotATB}

. Pick-p2rA: Prec = {p2ATrA, robotATA}, Add = {p2held}, Del = {p2ATrA}

. Pick-p2rB: Prec = {p2ATrB, robotATB}, Add = {p2held}, Del = {p2ATrB}

. Drop-p2rA: Prec = {p2held, robotATA}, Add = {p2ATrA}, Del = {p2held}

. Drop-p2rB: Prec = {p2held, robotATB}, Add = {p2ATrB}, Del = {p2held}

Too much repetition above; better to use action schemas . . .


Previous Example Encoded Using Schemas

New encoding uses variables to avoid repetition: variables replaced by constants of given types.

Types are K, for set of packages, and R, for rooms: K = {p1, p2}, R = {rA, rB}

Problem P = 〈F, I, O,G〉 can be expressed as:

• F = {atp(?p, ?r), atr(?r), holding(?p) | ?p ∈ K, ?r ∈ R}• I = {atp(p1, rA), atp(p2, rA), atr(rA)}• G = {atp(p1, rB), atp(p2, rB)},

• O contains 3 action schemas; use abbrev. P = Prec, A = Add, D = Del. Pick(?p ∈ K, ?r ∈ R): P : {atp(?p, ?r), atr(?r)}, A : {holding(?p)}, D : {atp(?p, ?r)}. Drop(?p ∈ K, ?r ∈ R): P : {holding(?p), atr(?r}, A : {atp(?p, ?r)}, D : {holding(?p)}. Move(?r1 ∈ R, ?r2 ∈ R): P : {atr(?r1)}, A : {atr(?r2)}, D : {atr(?r1)}

• Grounded actions obtained by replacing variables by constants of same type

• Symbols like atp, atr and holding called predicates

• Atoms like atp(p1, rA) obtained by replacing variables in atp(?p, ?r) by p1 and rA


PDDL: A Standard Syntax for Classical Planning Problems

• PDDL stands for Planning Domain Description Language

• Developed for International Planning Competition (IPC); evolving since 1998.

• In IPC, planners are evaluated over unseen problems encoded in PDDL

Problem in PDDL =⇒ Planner =⇒ Plan

• PDDL specifies syntax for problems P = 〈F, I,O,G〉 supporting STRIPS,variables, types, and much more

• Problems in PDDL specified in two parts:

. Domain: contains action and atom schemas along with argument types.

. Instance: contains initial situation, goal, and constants (objects) of eachtype


Example: 2-Gripper Problem in PDDL Syntax

(define (domain gripper)

(:requirements :typing)

(:types room ball gripper)

(:constants left right - gripper)

(:predicates (at-robot ?r - room)(at ?b - ball ?r - room)(free ?g - gripper)

(carry ?o - ball ?g - gripper))

(:action move

:parameters (?from ?to - room)

:precondition (at-robot ?from)

:effect (and (at-robot ?to) (not (at-robot ?from))))

(:action pick

:parameters (?obj - ball ?room - room ?gripper - gripper)

:precondition (and (at ?obj ?room) (at-robot ?room) (free ?gripper))

:effect (and (carry ?obj ?gripper) (not (at ?obj ?room)) (not (free ?gripper))))

(:action drop

:parameters (?obj - ball ?room - room ?gripper - gripper)

:precondition (and (carry ?obj ?gripper) (at-robot ?room))

:effect (and (at ?obj ?room) (free ?gripper) (not (carry ?obj ?gripper)))))

(define (problem gripper2)

(:domain gripper)

(:objects roomA roomB - room Ball1 Ball2 - ball)

(:init (at-robot roomA) (free left) (free right) (at Ball1 roomA)(at Ball2 roomA))

(:goal (and (at Ball1 roomB) (at Ball2 roomB))))


Computation: How to Solve Classical Planning Problems?

Problem in PDDL =⇒ Planner =⇒ Plan

• Planning is one of the oldest areas in AI; many ideas have been tried

• We focus on the two ideas that appear to work best computationally

. Planning as Heuristic Search

. Planning as SAT

• These methods able to solve problems over huge state spaces

• Of course, some problems are inherently hard, and for them, general, domain-independent planners unlikely to approach the performance of specializedmethods


Solving P by solving S(P ): Path-finding in graphs

Search algorithms for planning exploit the correspondence between (classical)states model and directed graphs:

• The nodes of the graph represent the states s in the model

• The edges (s, s′) capture corresponding transition in the model with same cost

In the planning as heuristic search formulation, the problem P is solved bypath-finding algorithms over the graph associated with model S(P )


Example: Simple Problem P and Reachable Graph S(P )

Problem P = 〈F, I,O,G〉 above where:

• F = {p, q, r}, I = {p}, G = {q, r}

• O with two actions a and b such that:

. Prec(a) = {p} , Add(a) = {q}, Del(a) = {}

. Prec(b) = {q} , Add(b) = {r}, Del(b) = {q}

Graph associated with reachable fragment of model S(P ); plan in red

p

p,q p,r

a

ba

p,q,ra

b a

Init

Goal


Search Algorithms for Path Finding in Directed Graphs

• Blind search/Brute force algorithms

. Goal plays passive role in the searche.g., Depth First Search (DFS), Breadth-first search (BrFS), Uniform Cost

(Dijkstra), Iterative Deepening (ID)

• Informed/Heuristic Search Algorithms

. Goals plays active role in the search through heuristic function h(s) thatestimates cost from s to the goal

e.g., A*, IDA*, Hill Climbing, Best First (BFS), LRTA*, . . .

Heuristic search algorithms can find paths in very large graphs; with more than1020 states as in Rubik’s cube).


Search Algorithms: General Scheme

Idea: pick node from search boundary Nodes containing initially root node. If nodeis goal, return solution. Else add children to boundary and repeat. Fail if boundaryis empty.

Algorithms like DFS, BrFS, and BFS instances of this general schema

• DFS: boundary is a stack, pick top, add on top

• BrFS: boundary is a queue, pick first, add last

• BFS: boundary is priority queue, pick node that min evaluation function f

. A*: f(s) = g(s) + h(s); g(s) is cost paid up to s, h(s) is estimated cost to goal

. WA*: f(s) = g(s) +Wh(s); W > 1

. Greedy BFS: f(s) = h(s)

Key dimensions: completeness, optimality, and complexity in time and space


On-Line Search: Learning Real Time A* (LRTA*):

• LRTA* is a very interesting real-time search algorithm

• It’s like hill-climbing where best child selected and others discarded, exceptthat heuristic V , initially V = h, updated dynamically

1. Evaluate each action a in s as: Q(a, s) = c(a, s) + V (s′)

2. Apply action a that minimizes Q(a, s)

3. Update V (s) to Q(a, s)

4. Exit if s′ is goal, else go to 1 with s := s′

• Two remarkable properties

. Each trial of LRTA gets eventually to the goal if space connected

. Repeated trials eventually get to the goal optimally, if h admissible!

• Also, it generalizes well to stochastic actions (MDPs)


Heuristics: where they come from?

• General idea: heuristic functions obtained as optimal cost functions of relaxed(simplified) problems

• Examples:

– Sum of Manhattan distances in N-puzzle– Number of misplaced blocks in Blocks-World– Euclidean Distance in Routing Finding– Spanning Tree in Traveling Salesman Problem

• Yet

– how to get and solve suitable relaxations?– how to get heuristics automatically for planning?


Heuristics for Classical Planning

• Key development in planning in the 90s: automatic extraction of heuristicfunctions to guide search for plans in S(P )

• Heuristics derived from relaxation where delete-lists of actions dropped

• This is called the delete-relaxation

• P (s) is P but with s as the initial state, P+ is delete-relaxation of P , andh∗P (s) is optimal cost to solve P from s. Then heuristic h(s) could be set to:

h(s)def= h∗P+(s)

• Yet, this doesn’t work computationally: solving P+(s) optimally as difficult assolving P (s) optimally (NP-hard)

• On the other hand, while solving relaxation P+(s) optimally is hard, just findingone solution not necessarily optimal, is easy


Basic Heuristics and Decomposition of Delete-Free Problems

If plan π1 achieves G1 and plan π2 achieves G2, π1, π2 achieves G1 and G2 in P+

Iterative procedure to compute plans for all atoms in P+(s) based on thisobservation:

• Atom p reachable in 0 steps with empty plan π(p) if p ∈ s• Atom p reachable i+ 1 steps with plan π(p1), . . . , π(pn), ap if

. p not reachable in i steps or less, and

. ∃ action ap that adds p with preconds p1, . . . , pn reachable in ≤ i steps

Properties: relaxed plans

• Procedure terminates in number of steps bounded by number of atoms

• If atom p reachable, π(p) is a relaxed plan for p; i.e. plan in P+(s)

• If atom p not reachable, there is no plan for p in P (s)

Basic Heuristics: hmax, hFF

• h(s) = i iff goal g reachable in i steps: is admissible heuristic called hmax• h(s) = number of different actions in π(g): is FF heuristic: hFF


Example

Problem P = 〈F, I,O,G〉 where

• F = {pi, qi | i = 0, . . . , n}

• I = {p0, q0}

• G = {pn, qn}

• O contains actions ai and bi, i = 0, . . . , n− 1:

. Prec(ai) = {pi} , Add(ai) = {pi+1}, Del(ai) = {pi}

. Prec(bi) = {qi} , Add(bi) = {qi+1}, Del(bi) = {qi}

Heuristics for state s = I where p0 and q0 are true:

. hmax(s) = n; hFF(s) = 2n; h∗(s) = 2n (optimal)

Yet if any atom pi or qi with i 6= n added to G,

. hmax(s) = n; hFF(s) = 2n; h∗(s) =∞


Max Heuristic and Layered Graphs

• Iterative reachability procedure above builds layers Pi of atoms from s:

P0 A0 P1 A1

...

...

...

P0 = {p ∈ s}Ai = {a ∈ O | Pre(a) ⊆ Pk, k ≤ i}

Pi+1 = {p ∈ Add(a) | a ∈ Ai, p 6∈ Pk, k < i+ 1}

The max heuristic is implicitly represented in layered graph:

hmax(s) = min i such each p ∈ G is in layer Pk, k ≤ i


Alternative, Mathematical Definition of Max Heuristic

• For all atoms p:

h(p; s)def=

{0 if p ∈ s, elsemina∈O(p)[cost(a) + h(Pre(a); s)]

• For sets of atoms C, set:

h(C; s)def= maxr∈Ch(r; s)

• Resulting heuristic function hmax(s):

hmax(s)def= h(Goals; s)


From Max to Sum: The Additive Heuristic

• For all atoms p:

h(p; s)def=

{0 if p ∈ s, elsemina∈O(p)[cost(a) + h(Pre(a); s)]

• For sets of atoms C, replace Max by Sum:

h(C; s)def=∑r∈C

h(r; s)

• Resulting heuristic function hadd(s):

hadd(s)def= h(Goals; s)

Heuristic hadd is not admissible like hmax but informative like hFF


State-of-the-art Planners: EHC, Helpful Actions, Landmarks

First generation of heuristic search planners, searched the graph defined by statemodel S(P ) using standard search algorithms like Greedy Best-First or WA*, andheuristics like hadd

More recent planners like FF, FD, and LAMA go beyond this in two ways:

• They exploit the structure of the heuristic and/or problem further:

. Helpful Actions: actions most relevant in relaxation

. Landmarks: implicit problem subgoals

• They use novel search algorithms

. Enforced Hill Climbing (EHC)

. Multi-Queue Best First Search

The result is that they can often solve huge problems, very fast


Enforced Hill Climbing Search in the FF Planner

FF planner works in two phases

• The second phase is a Greedy Best-First search guided by hFF. This iscomplete but slow

• First phase, called EHC (Enforced Hill Climbing) is incomplete but fast

. Starting with s = s0, EHC does a breadth-first search from s using onlythe helpful actions until a state s′ is found such that hFF(s

′) < hFF(s).. If such a state s′ is found, the process is repeated starting with s = s′. Else,

the EHC fails, and the second phase is triggered.

• An action is helpful in s if it is applicable in s and adds an atom p not in s suchthat p is a goal or the precondition of an action in the relaxed plan from s


Landmarks and Helpful Actions in LAMA’s Multi-queue BFS

• Standard best-first algorithms work with a single queue that is ordered accordingto the evaluation function f (f = h in GBFS, f = g + h in A*, f = g +W ∗ hin WA*, etc).

• If there are two or more evaluation functions f , it is also possible to haveseveral queues each one ordered by a different evaluation function

• Multi-queue Best-First search picks best node in one queue, then best nodein another queue, and so on, alternating

• LAMA uses four queues and two heuristics:

. two queues are ordered by hFF, and two by number of unachieved landmarks

. one queue in each pair is restricted to the nodes obtained by “helpful”actions


Landmarks and Multi-queue Best-First Search in LAMA (cont)

• Landmarks are implicit subgoals of the problem; formally atoms p that must betrue in all plans

• For example, clear(A) is a landmark in any Blocks problem where block A isabove a misplaced block.

• While finding all landmarks is computationally hard, some landmarks are easyto identify with methods similar to those used for computing heuristics

• Indeed, atom p is a landmark in P+(s), and hence of P (s), iff heuristics likehmax(s) becomes infinite once the actions that add p are excluded from theproblem.

• Thus, delete-relaxation landmarks can be computed in polynomial time; moreefficient methods than this, however, available (and used in LAMA).


Something Different: SAT Approach to Planning

• SAT is the problem of determining whether a set of clauses is satisfiable

• A clause is a disjunction of literals where a literal is an atom p or its negation ¬p

x ∨ ¬y ∨ z ∨ ¬w

• Many problems can be mapped into SAT

• SAT is intractable (exponential in the worst case unless P=NP) yet very largeSAT problems can be solved in practice


Planning as SAT

Maps problem P = 〈F,O, I,G〉 and horizon n into “clauses” C(P, n):

• Init: p0 for p ∈ I, ¬q0 for q ∈ F and q 6∈ I• Goal: pn for p ∈ G• Actions: For i = 0, 1, . . . , n− 1, and each action a ∈ O

ai ⊃ pi for p ∈ Prec(a)ai ⊃ pi+1 for each p ∈ Add(a)ai ⊃ ¬pi+1 for each p ∈ Del(a)

• Persistence: For i = 0, . . . , n − 1, and each atom p ∈ F , where where O(p+) and O(p−)

stand for the actions that add and delete p resp.pi ∧ ∧a∈O(p−)¬ai ⊃ pi+1

¬pi ∧ ∧a∈O(p+)¬ai ⊃ ¬pi+1

• Sequence: For each i = 0, . . . , n− 1, if a 6= a′, ¬(ai ∧ a′i)

– C(P, n) satisfiable iff there is a plan with length bounded by n

– Plan can be read from truth valuation that satisfies C(P, n).

– Encoding simple but not best computationally; for that: parallelism, NO-OPs, lower bounds

– Best current SAT planners are very good (Rintanen); potentially better than heuristic search

planners on highly constrained problems


AI Planning: Status

• The good news: classical planning works reasonably well

. Large problems can be solved fast (non-optimally)

• Model simple but useful

. Operators not primitive; can be policies themselves

. Fast closed-loop replanning able to cope with uncertainty sometimes

• Limitations

. Does not model Uncertainty (no probabilities)

. Does not deal with Incomplete Information (no sensing)

. Does not accommodate Preferences (simple cost structure)

. . . .


Beyond Classical Planning: Two Strategies

• Top-down: Develop solver for more general class of models; e.g., MarkovDecision Processes (MDPs), Partial Observable MDPs (POMDPs), . . .

+: generality−: complexity

• Bottom-up: Extend the scope of current ’classical’ solvers

+: efficiency−: generality

• We’ll do both, starting with transformations for

. compiling soft goals away (planning with preferences)

. compiling uncertainty away (conformant planning)

. deriving finite state controllers (usually set by hand)

. doing plan recognition (as opposed to plan generation)

. . . .


Planning with Soft Goals (Terminal Rewards)

• Soft goals as opposed to hard goals are to be achieved if worth the costs

• Utility of plan π is utility of soft goals p achieved minus plan cost:

u(π) =∑π|=p

u(p) −∑a∈π

c(a)

• Best plan achieves the hard goals while maximizing utility

• 2008 Int. Planning Competition featured soft goal planning track (net-benefit)

• Problem looks different than “classical” minimization of (positive) action costs

• Two choices to make: which soft goals to achieve and how


Soft Goal Planning with a Classical Planner: Transformation

Yet soft goals can be easily compiled away

• For each soft goal p, create new hard goal p′ initially false, and two newactions:

. collect(p) with precondition p, effect p′ and cost 0, and

. forgo(p) with an empty precondition, effect p′ and cost u(p)

• Plans π maximize u(π) iff minimize c(π) =∑a∈π c(a) in translation

• Classical planners over translation outperform native net-benefit planners

• This transformation is simple and polynomial; others are neither but still useful


Goal Recognition with a Planner

S

A B C

D

F EH

J

• Agent can move one unit in the four directions

• Possible targets are A, B, C, . . .

• Starting in S, he is observed to move up twice

• Where is he going? Why?


Goal Recognition with a Planner: Formulation

S

A B C

D

F EH

J

• From Bayes, goal posterior is P (G|O) = αP (O|G)P (G), G ∈ G

• P (O|G) measures how well goal G predicts observations O, defined asmonotonic function of difference between two costs:

. c(G+O): cost of achieving G while complying with obs O

. c(G+O): cost of achieving G while not complying with obs O

• These costs can be computed by classical planner after transformation; goalposterior P (G|O) results from |G| calls to classical planner (assuming priorsP (G) given).


Goal Recognition Example

1 2 3 4 5 6 7 8 9 10 11

1

2

3

4

5

6

7

8

9

10

11

A

B C D E

F

I

6

11

3

Time StepsP(

G|O t

)

1 2 3 4 5 6 7 8 9 10 11 12 130

0.25

0.5

0.75

1

G=AG=BG=CG=DG=EG=F

Grid shows ‘noisy walk’ and possible targets; curves show resulting posteriorprobabilities P (G|O) of each possible target G as function of time

Posterior probabilities P (G|O) obtained from Bayes’ rule and costs computed byclassical planner


Incomplete Information: Conformant Planning

GI

Problem: A robot must move from an uncertain I into G with certainty, one cellat a time, in a grid nxn

• Problem very much like a classical planning problem except for uncertain I

• Plans, however, quite different: best conformant plan must move the robotto a corner first (localization)


Conformant Planning: Belief State Formulation

GI

• call a set of possible states, a belief state

• actions then map a belief state b into a bel state ba = {s′ |s′ ∈ F (a, s) & s ∈ b}

• conformant problem becomes a path-finding problem in belief space

Problem: number of belief state is doubly exponential in number of variables.

– effective representation of belief states b

– effective heuristic h(b) for estimating cost in belief space

Recent alternative: translate into classical planning . . .


Basic Translation: Move to the ’Knowledge Level’

Given conformant problem P = 〈F,O, I,G〉

• F stands for the fluents in P

• O for the operators with effects C → L

• I for the initial situation (clauses over F -literals)

• G for the goal situation (set of F -literals)

Define classical problem K0(P ) = 〈F ′, O′, I ′, G′〉 as

• F ′ = {KL,K¬L | L ∈ F}• I ′ = {KL | clause L ∈ I}• G′ = {KL | L ∈ G}• O′ = O but preconds L replaced by KL, and effects C → L replaced by KC → KL

(supports) and ¬K¬C → ¬K¬L (cancellation)

K0(P ) is sound but incomplete: every classical plan that solves K0(P ) is aconformant plan for P , but not vice versa.


Key elements in Complete Translation KT,M(P )

• A set T of tags t: consistent sets of assumptions (literals) about the initialsituation I

I 6|= ¬t

• A set M of merges m: valid subsets of tags (= DNF)

I |=∨t∈m

t

• New (tagged) literals KL/t meaning that L is true if t true initially


A More General Translation KT,M(P )

Given conformant problem P = 〈F,O, I,G〉

• F stands for the fluents in P

• O for the operators with effects C → L

• I for the initial situation (clauses over F -literals)

• G for the goal situation (set of F -literals)

Define classical problem KT,M(P ) = 〈F ′, O′, I ′, G′〉 as

• F ′ = {KL/t , K¬L/t | L ∈ F and t ∈ T}• I ′ = {KL/t | if I |= t ⊃ L}• G′ = {KL | L ∈ G}• O′ = O but preconds L replaced by KL, and effects C → L replaced by KC/t → KL/t

(supports) and ¬K¬C/t→ ¬K¬L/t (cancellation), and new merge actions∧t∈m,m∈M

KL/t → KL

The two parameters T and M are the set of tags (assumptions) and the set of merges (valid sets

of assumptions) . . .


Compiling Uncertainty Away: Properties

• General translation scheme KT,M(P ) is always sound, and for suitable choice ofthe sets of tags and merges, it is complete.

• KS0(P ) is complete instance of KT,M(P ) obtained by setting T to the set ofpossible initial states of P

• Ki(P ) is a polynomial instance of KT,M(P ) that is complete for problemswith conformant width bounded by i.

. Merges for each L in Ki(P ) chosen to satisfy i clauses in I relevant to L

• The conformant width of most benchmarks bounded and equal 1!

• This means that such problems can be solved with a classical planner after apolynomial translation


Derivation of Finite State Controllers Using Planners

• Starting in A, move to B and back to A; marks A and B observable.

A B

• This finite-state controller solves the problem

q0

A/Right-/Right

q1B/Left

-/Left

• FSC is compact and general: can add noise, vary distance, etc. and still works

• Heavily used in practice, e.g. video-games and robotics, but written by hand


Derivation of Finite State Controllers Using Planners: Idea

• FSC maps controller state, observation pair into action, controller state pair

(q, o) 7→ (a, q′)

• For deriving FSC using planner, introduce “actions” (q, o, a, q′) for reducingoriginal problem P with sensing into conformant problem P ′

• Action (q, o, a, q′) behaves like action a but conditional on q and o being true,making q′ true as well

• Actions (q, o, a, q′) in the plan for transformed problem P ′ encode finite-statecontroller that solves P

• Plan for conformant P ′ can be obtained by running classical planner on furthertransformed problem problem K(P ′)


Finite State Controller: Learning from a Single Example

q0

TB/Up-B/Up

TC/Right

q1-C/Down

TB/Right

-B/Down

• Example: move ‘eye’ (circle) one cell at a time til green block found

• Observables: Whether cell ‘seen’ contains a green block (G), non-green block(B), or neither (C); and whether on table (T) or not (–)

• Controller shown derived using a classical planner after transformations

• Derived controller is general and applies not only to instance shown but to anyother problem instance; i.e., any number of blocks and any configuration


Other Problems Solved by Transformations and ClassicalPlanners

• Temporally Extended Goals expressed in LTL like “monitor room A and roomB forever”: 2(3At(A) ∧3At(B))

• Probabilistic Conformant Planning: find action sequence that achieves goalwith threshold probability

• Off-line planning with partial observability: Contingent planning

• On-line planning with partial observability: Wumpus, Minesweeper, . . .

• Multiagent planning problems; e.g., agent 1 and 2 need to find blocks 1 and2 resp. hidden in some room; what they should communicate and when?

• . . .


Planning with Markov Decision Processes: Goal MDPs

MDPs are fully observable, probabilistic state models:

• a state space S


• a set G ⊆ S of goal states





– Optimal solutions minimize expected cost from s0 to goal


Discounted Reward Markov Decision Processes

Another common formulation of MDPs . . .

• a state space S




• rewards r(a, s) positive or negative

• a discount factor 0 < γ < 1 ; there is no goal


– Optimal solutions max expected discounted accumulated reward from s0


Expected Cost/Reward of Policy (MDPs)

• In Goal MDPs, expected cost of policy π starting in s, denoted as V π(s), is

V π(s) = Eπ[∑si

c(ai, si) | s0 = s, ai = π(si) ]

where expectation is weighted sum of cost of possible state trajectories timestheir probability given π

• In Discounted Reward MDPs, expected discounted reward from s is

V π(s) = Eπ[∑si

γi r(ai, si) | s0 = s, ai = π(si)]

• In both cases, optimal value function V ∗ expresses V π for best π


Solving MDPs: Assumptions, Equivalence

Conditions that ensure existence of optimal policies and correctness (convergence)of some of the methods we’ll see:

• For discounted MDPs, 0 < γ < 1, none needed as everything is bounded; e.g. discounted

cumulative reward no greater than C/1− γ, if r(a, s) ≤ C for all a, s

• For goal MDPs, absence of dead-ends assumed so that V ∗(s) 6=∞ for all s

Discounted Reward MDPs easy to convert into equivalent Goal MDPs; nosimilar transformation known in the opposite direction. For conversion:

1. multiply rewards by −12. add sufficiently high constant to make costs positive

3. multiply transition probabilities Pa(s′|s) by γ

4. add dummy goal g with probabilities Pa(g|s) = (1− γ) for all a and s, a ∈ A(s)


Basic Dynamic Programming Methods: Value Iteration (1)

• Greedy policy πV for V = V ∗ is optimal:

πV (s) = arg mina∈A(s)[c(s, a) +∑s′∈S

Pa(s′|s)V (s′)]

• Optimal V ∗ is unique solution to Bellman’s optimality equation for MDPs

V (s) = mina∈A(s)

[c(s, a) +∑s′∈S

Pa(s′|s)V (s′)]

with V (s) = 0 for goal states s

• For discounted reward MDPs, Bellman equation is

V (s) = maxa∈A(s)

[r(s, a) + γ∑s′∈S

Pa(s′|s)V (s′)]


Basic DP Methods: Value Iteration (2)

• Value Iteration finds V ∗ solving Bellman eq. by iterative procedure:

. Set V0 to arbitrary value function; e.g., V0(s) = 0 for all s

. Set Vi+1 to result of Bellman’s right hand side using Vi in place of V :

Vi+1(s) := mina∈A(s)

[c(s, a) +∑s′∈S

Pa(s′|s)Vi(s′)]

• Vi 7→ V ∗ as i 7→ ∞

• V0(s) must be initialized to 0 for all goal states s


(Parallel) Value Iteration and Asynchronous Value Iteration

• Value Iteration (VI) converges to optimal value function V ∗ asympotically

• Bellman eq. for discounted reward MDPs similar, but with max instead of min,and sum multiplied by γ

• In practice, VI stopped when residual R = maxs |Vi+1(s)−Vi(s)| is small enough

• Resulting greedy policy πV has loss bounded by 2γR/1− γ

• Asynchronous Value Iteration is asynchronous version of VI, where statesupdated in any order

• Asynchronous VI also converges to V ∗ when all states updated infinitely often;it can be implemented with single V vector


Basic DP Methods: Policy Iteration (1)

• Expected cost V π(s) can be characterized as solution to Bellman equation

V π(s) = c(a, s) +∑s′∈S

Pa(s′|s)V π(s′)

where a = π(s), and V π(s) = 0 for goal states

• This set of linear equations can be solved analytically, or by VI-like procedure

• Optimal expected cost V ∗(s) is minπ Vπ(s) and optimal policy is the arg min

• For discounted reward MDPs, all similar but with r(s, a) instead of c(a, s), maxinstead of min, and sum discounted by γ


Basic DP Methods: Policy Iteration (2)

• Let Qπ(a, s) be expected cost from s when doing a first and then π

Qπ(a, s) = c(a, s) +∑s′∈S

Pa(s′|s)V π(s′)

• When Qπ(a, s) < Qπ(π(s), s), π strictly improved by changing π(s) to a

• Policy Iteration (PI) computes π∗ by seq. of evaluations and improvements

1. Starting with arbitrary (proper) policy π

2. Compute V π(s) for all s (evaluation)

3. Improve π by setting π(s) to a if Qπ(a, s) < Q(π(s), s) for a = arg mina∈A(s)Qπ(a, s)

(improvement)

4. If π changed in 3, go back to 2, else finish

• PI finishes with π∗ after finite number of iterations, as # of policies is finite


Dynamic Programming and Heuristic Search

• Dynamic programming methods like Value and Policy Iteration are exhaustive;need vectors of size |S|

• Heuristic search algorithms like A* are incremental, and with good admissibleheuristics can solve much larger problems optimally like Rubik’s Cube

• Can admissible heuristics (lower bounds) and initial state s0 be used forfocusing updates in DP methods?

• Convergence to V ∗ over all s not needed for optimality from given s0

Theorem 1. If V is an admissible value function s.t. the residuals over thestates reachable with πV from s0 are all zero, then πV is an optimal policy froms0 (i.e. it minimizes V π(s0))


Learning Real Time A* (LRTA*) Revisited

1. Evaluate each action a in s as: Q(a, s) = c(a, s) + V (s′)




• LRTA* can be seen as asynchronous value iteration algorithm for deterministicactions that takes advantage of theorem above (i.e. updates = DP updates)

• Convergence of LRTA* to V implies residuals along πV reachable states froms0 are all zero

• Then 1) V = V ∗ along such states, 2) πV = π∗ from s0, but 3) V 6= V ∗ andπV 6= π∗ over other all states; yet this is irrelevant given s0


Real Time Dynamic Programming (RTDP) for MDPs

RTDP is a generalization of LRTA* to MDPs due to (Barto et al 95); just adaptBellman equation used in the Eval step

1. Evaluate each action a applicable in s as

Q(a, s) = c(a, s) +∑s′∈S

Pa(s′|s)V (s

′)



4. Observe resulting state s′


Same properties as LRTA* but over MDPs: after repeated trials, greedy policyeventually becomes optimal if V (s) initialized to admissible h(s)


A General DP + Heuristic Search Scheme for MDPs

• Let ResV (s) be residual for s given admissible value function V

• Optimal π for MDPs from s0 can be obtained for sufficiently small ε > 0:

1. Start with admissible V ; i.e. V ≤ V ∗2. Repeat: find s reachable from πV & s0 with ResV (s) > ε, and Update it

3. Until no such states left

• V remains admissible (lower bound) after updates

• Number of iterations until ε-convergence bounded by∑s∈S[V

∗(s)− V (s)]/ε

• Like in heuristic search, convergence achieved without visiting or updatingmany of the states in S

• Heuristic search MDP algorithms like LRTDP, ILAO*, HDP, LDFS, etc. areall instances of this general schema


Scaling Up to larger MDPs: A Little Map

• Off-line Methods: Compute complete policies or complete policies from s0

. Dynamic programming: VI, PI

. Heuristic search: RTDP, LAO*, Find-and-Revise, HDP, LDFS, . . .

• On-line Methods: Compute action to do in current state state

. Finite-Horizon Relaxation: Solved anytime UCT, RDTP, AO*, . . .

. Deterministic Relaxation: Solved using classical planners like FF-Replan

• Alternative Off-Line Methods:

. FOND Relaxation: Strong cyclic plans yield proper policies; e.g., PRP

. Function Approximation: Parameterized value function; common in RL

. Symbolic Methods: Compact, symbolic representation of value function


Partially Observable MDPs: Goal POMDPs

POMDPs are partially observable, probabilistic state models:

• states s ∈ S




• set of observable target states SG



– Belief states are probability distributions over S

– Solutions are policies that map belief states into actions

– Optimal policies minimize expected cost to go from b0 to target bel state.


Discounted Reward POMDPs

A common alternative formulation of POMDPs:

• states s ∈ S





• rewards r(a, s) positive or negative

• discount factor 0 < γ < 1 ; there is no goal

– Optimal policies max expected discounted accumulated reward from b0

– Discounted Reward POMDPs can be converted into equivalent Goal POMDPs

– Qualitative or Logical POMDPs result from dropping the probabilities; calledcontingent planning or planning with partial observability


POMDPs are MDPs over Belief Space

• Beliefs b are probability distributions over S

• An action a ∈ A(b) maps b into ba

ba(s) =∑s′∈S

Pa(s|s′)b(s′)

• The probability of observing o then is:

ba(o) =∑s∈S

Pa(o|s)ba(s)

• . . . and the new belief is

boa(s) = Pa(o|s)ba(s)/ba(o)


Computational Methods for POMDPs

• Exact Methods

. Value Iteration (VI) over piecewise linear functions

• Approximate and On-Line Methods:

. Point-based Value Iteration Methods: VI over few belief points

. RTDP-Bel: RTDP applied to discretized beliefs

. PO-UCT: UCT applied to action observation histories

• Logical Methods: probabilities dropped; beliefs as sets of states

. Compilations and relaxations for action selection

. Belief tracking: for determining truth of action preconditions and goals


Value Iteration for POMDPs

• V π given by Bellman eq. with c(a, b) =∑s∈S c(a, s)b(s) and V π(b) = 0 for goal

beliefs

V π(b) = c(a, b) +∑o∈O

ba(o)Vπ(boa)

• Optimal V ∗ given by solution to Bellman opt. eq. with V (b) = 0 for goal bels b

V (b) = mina∈A(b)

[c(a, b) +∑o∈O

ba(o)V (boa)]

• Problem is infinite and dense space of beliefs to update

• VI and Point-based VI methods for POMDPs start with compact piecewiselinear and concave (pwlc) function V0, that remains pwlc after updates

• Number of “pieces” (vectors), however, grow exponentially with number ofupdates


Approximate POMDP Methods: RTDP-Bel

Since POMDPs are MDPs over belief space, RTDP algorithm for POMDPs becomes

RTDP-Bel

% Initial value function V given by heuristic h

% Changes to V stored in a hash table using discretization function d(·)

Let b := b0 the initial belief

Sample state s with probability b(s)

While b is not a goal belief doEvaluate each action a ∈ A(b) as: Q(a, b) := c(a, b) +

∑o∈O ba(o)V (boa)

Select best action a := argmina∈A(b)Q(a, b)

Update value V (b) := Q(a, b)

Sample next state s′ with probability Pa(s′|s) and set s := s′

Sample observation o with probability Pa(o|s)Update current belief b := boa

end while

RTDP-Bel discretizes beliefs b for writing to and reading from hash table


Belief Tracking

• Exact, Explicit Flat Methods

. Even for applying given belief policy, one needs to compute the belief bi at time i. This is

exponential in number of variables both in probabilistic and logical settings

• Exact, Lazy Approaches for Planning

. Global beliefs b not strictly required, rather beliefs on preconditions and goals

. In logical setting, this can be cast as SAT problem

. In probabilistic setting, as Dynamic Bayesian Network inference problem

. Both approaches still exponential in worst case, but can be sufficiently practical

• Approximations:

. Particle filtering: when uncertainty in dynamics and sensing represented by probabilities

. Structured methods: when uncertainty in dynamics and sensing represented by sets


Probabilistic Belief Tracking with Particles: Basic Approach

• Computing next belief boa is exponential in |S|

boa(s) = Pa(o|s)ba(s)/ba(o)ba(s) =

∑s′∈S Pa(s|s′)b(s′)

ba(o) =∑s∈S Pa(o|s)ba(s)

• Particle filtering algorithms approximate b by (multi)set of unweighted samples

. probability of X = x approximated by ratio of samples in b where X = x is true

• Approx. belief Bk+1 obtained from (multi)set of samples Bk, action a, and obso in two steps:

. Sample sk+1 from S with probability Pa(sk+1|sk) for each sk in Sk

. (Re)Sample new set of samples by sampling each sk+1 with weight P (o|ss+1)

• Potential problem is that particles may die out if many probabilities are zero


Beliefs, Width, Translations for On-Line Planning

Belief tracking is exponential in # of variables but structure can reduce this:

• Exact bel tracking algorithms that are exponential in max number of variables all relevant to

a given precondition or goal (width)

• Approximate bel tracking algorithms exponential in max number of variables all causallyrelevant to a given precondition, goal, or observable (causal width)

• Polynomial translations into classical problems based on width-considerations used for on-lineplanning

POMDP methods can yield good quality actions but trouble scaling up. Translation-based

methods better at scaling up but worse in quality

1 211 123

1 111211

1 112222211 22 21112 33 1

P I T

P I T

Minesweeper Wumpus Problem


Summary

• Planning is the model-based approach to autonomous behavior

• Many models and dimensions; all intractable in worst case

• Challenge is mainly computational, how to scale up

• Lots of room for ideas whose value must be shown empirically

• Key technique in classical planning is automatic derivation and use of heuristics

• Power of classical planners used for other tasks via transformations

• Structure and relaxations also crucial for planning with sensing

• Promise: a solid methodology for autonomous agent design


Some Challenges

• Classical Planning

. states & heuristics h(s) not black boxes; how to exploit structure further?

. on-line planners to compete with state-of-the-art classical planners

• Probabilistic MDP & POMDP Planning

. inference can’t be at level of states or belief states but at level of variables

• Multiagent Planning

. should go long way with single-agent planning and plan recognition; gametheory seldom needed

• Hierarchical Planning

. how to infer and use hierarchies; what can be abstracted away and when?


Best first search can be pretty blind

Small DoorLarge Door

• Problem involves agent that has to get large package through one of two doors

• The package doesn’t fit through the closest door


• Numbers in cells show number of states expanded where agent at that cell

• Algorithm is greedy best first search with additive heuristic

• Number of state expansions is close to 998; FF expands 1143 states, LAMA more!

• 34 different states expanded with agent at target, only last with pkg!

Best first search can be pretty blind: Doors Problem


References

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[3] F. Bacchus and F. Kabanza. Using temporal logics to express search control knowledge for planning. ArtificialIntelligence, 116:123–191, 2000.

[4] J. A. Baier, F. Bacchus, and S. A. McIlraith. A heuristic search approach to planning with temporally extendedpreferences. Artificial Intelligence, 173(5–6):593–618, 2009.

[5] A. Barto, S. Bradtke, and S. Singh. Learning to act using real-time dynamic programming. Artificial Intelligence,72:81–138, 1995.

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[12] B. Bonet and H. Geffner. Solving POMDPs: RTDP-Bel vs. point-based algorithms. In Proc. 21st Int. Joint Conf.on Artificial Intelligence, pages 1641–1646, 2009.

[13] B. Bonet and H. Geffner. Planning under partial observability by classical replanning: Theory and experiments. InProc. 22nd Int. Joint Conf. on Artificial Intelligence, pages 1936–1941, 2011.


[14] B. Bonet and H. Geffner. Action selection for MDPs: Anytime AO* versus UCT. In Proc. 26nd Conf. on ArtificialIntelligence, pages 1749–1755, 2012.

[15] B. Bonet and H. Geffner. Causal belief decomposition for planning with sensing: Completeness and practicalapproximation. In Proc. 23rd Int. Joint Conf. on Artificial Intelligence, 2013.

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[17] B. Bonet, H. Palacios, and H. Geffner. Automatic derivation of memoryless policies and finite-state controllersusing classical planners. In Proc. 19th Int. Conf. on Automated Planning and Scheduling, pages 34–41, 2009.

[18] C. Boutilier, R. Reiter, and B. Price. Symbolic dynamic programming for first-order MDPs. In Proc. 17th Int.Joint Conf. on Artificial Intelligence, pages 690–700, 2001.

[19] M. Bowling, R. Jensen, and M. Veloso. A formalization of equilibria for multiagent planning. In Proc. 18th Int.Joint Conf. on Artificial Intelligence, pages 1460–1462, 2003.

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AAAI-2014 Tutorial A Concise Introduction to Planning