Of Programs, Plans, and Automata:
101 things you can do with AI planning
Sheila A. McIlraith
Joint work with
Jorge Baier, Christian Fritz & Shirin Sohrabi
University of Toronto
Toronto, Canada
Oxford University,
February 24, 2015
Sheila A. McIlraith (UofT): Of Plans, Programs, and Automata 1 / 71

...some great graduate students and collaborators ...
Jorge Baier Shirin Sohrabi Christian Fritz Tran Cao Son
Meghyn Bienvenu Aws Albarghouthis Niloofar Razavi
Nathan Robinson
Yixiao Wang
Sotirios Liaskos

It’s been 50 years ...
2015 marks the 50th anniversary of the Shakey Project, which resulted
in such innovations as A* search and STRIPS planning.
We’ve seen substantial advances in AI Automated Planning since that
time:
compact state and transition system encodings
advances in heuristic search and SAT
plans with hundreds of actions
... hundreds of objects
... 21000
states
plan computation ranging from milliseconds to a couple of hours

It’s been 50 years ...
2015 marks the 50th anniversary of the Shakey Project, which resulted
in such innovations as A* search and STRIPS planning.
We’ve seen substantial advances in AI Automated Planning since that
time:
compact state and transition system encodings
advances in heuristic search and SAT
plans with hundreds of actions
... hundreds of objects
... 21000
states
plan computation ranging from milliseconds to a couple of hours
Top-performing planners exploit heuristic search, though
planning-tailored SAT-based planners are starting to close the gap.
Most of these advances have been in classical planning.

Automated Planning
Given (F, O, I, G), where:
F, set of propositions
O, transitions system described in terms of a set of operators
E.g, pickup(X)
Precondtion: handEmpty
Eﬀect: holding(X), ¬handEmpty
I ∈ L(F), initial state description
G ∈ L(F), goal condition

Automated Planning
E.g, pickup(X)
Planning Problem:
Find an ordering of operators which if applied in I lead to a state
satisfying G

Automated Planning
E.g, pickup(X)
Planning Problem:
Find an ordering of operators which if applied in I lead to a state
satisfying G
Complexity:
PSPACE-complete [Bylander, 1994]

Classical Planning
Given:
Initial state (fully described)
Goal condition (condition about ﬁnal state)
Operators (deterministic)
Task:
Find any sequential plan

Non-Classical Planning
Non-classical planning can take many forms:
Given:
Initial state (partially described)
Goal condition (temporally extended goal and/or preferences, ...)
Operators (non-deterministic actions, complex actions, ...)
Task:
Find a (high quality) plan

Claims

Claims
1st Claim
Many tasks that involve reasoning about dynamical systems can be cast
as non-classical planning problems (and indeed many compelling
real-world planning applications are non-classical).

Claims
1st Claim
as non-classical planning problems (and indeed many compelling
real-world planning applications are non-classical).
2nd Claim
We can exploit state-of-the-art classical planners to eﬀectively solve
non-classical planning problems.

The 1st Claim
as non-classical planning problems. E.g.,
(customized) web service composition
synthesis of business processes
veriﬁcation & counter-example generation
diagnosis of dynamical systems
explanation generation
activity recognition, plan understanding
narrative generation
stakeholder goal modeling
test generation for concurrent programs
network topology transformation
...
and at least 91 other tasks!

Spoiler Alert
Key Enabler:
It is the richness of the statement of the planning objective that
enables us to view 101 (and more) diverse endeavours as instances of

Spoiler Alert
Key Enabler:
These objectives can be speciﬁed via
Linear Temporal Logic

Spoiler Alert
Key Enabler:
Automata

Spoiler Alert
Key Enabler:
Automata
Regular Expressions in the form of (Con)Golog

Spoiler Alert
Key Enabler:
Automata
Hierarchical Task Networks (HTN)

Spoiler Alert
Key Enabler:
Automata
Hierarchical Task Networks (HTN)
and as either temporally extended goals (aka domain control
knowledge) or as preferences.

LTL in a Nutshell
A modal temporal logic with modalities referring to time. Interpreted
over sequences of states (paths).
Syntax
if f ∈ F then f is a LTL formula
if φ and ψ are LTL formulae then so are ¬φ, φ ∨ ψ, ...
formulae constructed with the following temporal modalities:
future: U(until), (next), (always), ♦(eventually)
past: S(since), •(yesterday), (always past), (eventually past)
E.g., Always have your keys and eventually be at home.
(have(keys)) ∧ ♦(at(home))
(e.g., [Pnueli, 1977])

Example - Diagnosis
Example 1 - Diagnosis [Sohrabi, Baier, M, KR2010, AAAI11]
Observations
I started my car this morning; drove to work; on the way to work I
bought $5 worth of gas; I hit a pothole; the radio said it was -20
Celsius; I parked outside. At noon, I picked up my bag from the
trunk of the car. At the end of the day, my car would not start. I
checked the radio and it was still working.
What’s the explanation for my car not starting?
Battery died
Punctured gas tank, then ran out of gas
Starter motor broke
: 8[Sohrabi, Baier, M KR2010, AAAI11]

Diagnosis as Non-Classical Planning
Dynamical Diagnosis
• Initial State
• Goal State
• Transition System
obsnobs1
System Description
obs2 obs3 . . .
Observations
(i.e., multiple partial states)

Diagnosis as Non-Classical Planning
Dynamical Diagnosis
• Initial State
• Goal State
• Transition System
obsnobs1
a1 a2 a3 ak
Diagnosis = {Assumptions, {a1, a2, a3, …, ak}}
System Description
obs2 obs3 . . .
Observations
(i.e., multiple partial states)

Example - Test Generation for Concurrent Programs
...
T1 T2 Tk serialization
[Razavi,Farzan,M,VVPS11,STTP14]

Example - Synthesis of Business Processes
Process Order
PrePay
Account
Invoice Out Source
Manufacture
. . .
Fill Order Ship GoodsProcess Payment

Example 4 - Web Service Composition
Web Services (WS)
Web Services are Web-accessible programs and devices.

Automating Web Service Composition (WSC)
Problem: Automatically select and compose services to achieve a
user’s objectives.
E.g.,
Make the travel arrangements for my IJCAI 2015 conference
1
Approach: non-classical planning objective comprising:
1. A generic procedure or script to specify roughly how to
perform the task.
2. A set of hard constraints and preferences that customize and
optimize for the individual user.
[M, Son, IEEE01, KR02]
[M, Son, IEEE01, KR2002]

User Preferences
Why is this Hard ?
E.g., preferences can be
temporally-extended
• I prefer to always pay with my American Express credit card.
conditional
• If my return flight departs before 9am, then I prefer to stay in a hotel
located at the airport the night before departure.
interacting and/or mutually inconsistent
• I prefer to stay in one hotel for my entire trip.
• I prefer to stay at the conference hotel.
• I prefer to spend $100/night or less on my hotel room.
nested
• I prefer to always book my flight after my hotel reservation is
confirmed, if booking is being done 2 weeks or more prior to the trip.
1. User preferences can be arbitrarily complex and mutually inconsistent.
[Sohrabi, M, ISWC06,10][Sohrabi, Baier, M, IJCAI09]

Historically, planning with LTL, Golog, and HTN
required special-purpose planners.

The 2nd Claim
Question:
Can we leverage the beneﬁts of classical approaches for these
compelling non-classical planning problems?
Claim:
In many cases it is possible to reformulate a non-classical
planning problem in order to apply directly or almost directly
successful classical approaches, yielding superior performance.
[Jorge Baier, Thesis 2010]

We Address Compelling Non-Classical Problems
We have addressed the following problems:
Planning for LTL Temporally Extended Goals (TEGs)
Planning with LTL Temporally Extended Preferences (TEPs)
Planning with restricted RE’s – Golog-like Procedural Control
Planning with RE’s plus concurrency – ConGolog Procedural Control
Planning with Golog Sensing Procedures (not discussed)
Planning with Golog-like Procedural Preferences (not discussed)
Key Enabler: Translation to automata or petri-nets in a manner that
supports the exploitation of heuristic search planners

Common Approach: Reformulation
Reformulated
Non−classical
planning task
planning task
Reformulation
J. Baier: Eﬀective Search Techniques for Non-Classical Planning via Reformulation 7 / 19

Reformulated
Handled by existing solver? Is not handled by existing solver?
Non−classical
planning task
planning task
Reformulation
Planner
Modified
Planner
Existing

The Rest of the Talk

Planning for LTL Temporally Extended Goals
Main Results:
1 First-order, finite logic to represent TEGs: f-FOLTL
(Following [Cerrito, Mayer, Praud, LPAR99])
2 Method to compactly convert TEGs into final-state goals
Algorithm: f-FOLTL formula → parameterized automaton
Theorem: Algorithm is correct
Theorem: Translation worst-case exponential
Property: Simplification and parameterization avoids worst-case
blowup in practice.
Representation of the automaton in a planning problem
3 Experimental evaluation: showing orders-of-magnitude
improvements over traditional approaches.
[Baier, M, AAAI2006]

From f-FOLTL to Parameterized NFA
Example PNFA for (∀x). (ECapital(x) ⊃ ♦at(x)) ∧ ♦ at(Paris).
Paris
C2 NYC
C1
at(x) at(Paris)
NYC
(¬ECapital(x) ∨ at(x)) ∧ at(Paris)
⊤ ⊤
Plan: nil;
Objects: C1, C2, Paris, NYC. ECapital = {C1, C2, Paris}
Initial State: {at(NYC)}

Paris
C2 NYC
C1
at(x) at(Paris)
NYC
⊤ ⊤
Plan: nil;

Paris
C2 NYC
C1
at(x) at(Paris)C1
NYC
⊤ ⊤
Plan: FlyTo(C1);

Paris
C2 NYC
C1
at(x) at(Paris)C1
C2 NYC
⊤ ⊤
Plan: FlyTo(C1); FlyTo(C2);

From f-FOLTL to Parameterized NFA (PNFA)
Paris
C2 NYC
C1
at(x) at(Paris)C1
C2 NYC
Paris
C1
C2 NYC
Paris
⊤ ⊤
Plan: FlyTo(C1); FlyTo(C2); FlyTo(Paris);

From PNFA to PDDL
Key Idea: Represent the PNFA within the planning domain.
We update original problem with:
New Predicates represent what objects are in what states
New Updates in Actions for the new predicates
New (Classical) Goal based on the accepting condition of the
automaton
Augmented Initial State

Experimental Evaluation
Our Reformulation + FFX [Thiebaux, Hoffman, Nebel, 2005]
versus
TLPLAN [Bacchus & Kabanza, 1998]
... on standard benchmarks extended with TEGs.
Experimental Evaluation
Our Reformulation + FFX (Thiébaux, Hoffmann, & Nebel, 2005)
versus
TLPlan (Bacchus & Kabanza, 1998)
... on standard benchmarks extended with TEGs.
Domain Speedup
< 2 times
faster
2-10 times
faster
1 order of
magn.
2 orders of
magn.
≥ 3 orders
of magn.
ZenoTravel 0 11% 22% 56% 11%
Logistics 6% 24% 24% 35% 12%
Robot 0 44% 33% 22% 0
Translation time never exceeds 15% of planning time.
Ratio |Automatonϕ|/|ϕ| never exceeds 1.
Solution lengths are comparable.
Translation time never exceeds 15% of planning time.
Ratio |Automatonϕ|/|ϕ| never exceeds 1.
Solution lengths are comparable.

Planning with LTL Temporally Extended Preferences
Main Results:
1 Compilation of TEPs in PDDL3 to ﬁnal-state preferences.
2 Branch-and-bound algorithm that ﬁnds plans of increasing quality
3 Suite of heuristics, based on existing heuristics
4 Formal analysis:
conditions for optimality and k-optimality.
5 Implemented system: HPlan-P (IPC Runner Up)
6 Experimental analysis that shows that:
Heuristic and pruning are key to good performance
Our planner is more consistent than the competition winner
[Baier, Bacchus, M, AIJ2009]

But what about (action-centric) procedural control?

Planning with Regular Expressions+
In planning, experts often know how to solve a problem, e.g.:
“If not at point A, then drive the truck to point A; while there
are packages at point A, pick a package and load it onto the
truck; drive to point B; while there are packages on the truck,
pick a package and unload it from the truck.”
We call this search control.
It can drastically increase planning eﬃciency.

Golog
One language for specifying search control is Golog:
if not at(A) then driveTo(A);
while (∃pkg)at(pkg, A) do πpkg.load(pkg);
driveTo(B);
while (∃pkg)onTruck(pkg) do πpkg.unload(pkg)

Golog and ConGolog: Syntax
Golog:
a primitive action
φ? test condition φ
(δ1; δ2) sequence
if φ then δ1 else δ2 conditional
while φ do δ loops
(δ1|δ2) non-deterministic choice
πv.δ non-deterministic choice of argument
δ∗
non-deterministic iteration
{P1(t1, δ1); . . . ; Pn(tn, δn); δ} procedures
ConGolog: (in addition)
(δ1 δ2) concurrent execution
(δ1 δ2) prioritized concurrency
δ concurrent iteration

ConGolog: Semantics
The semantics is deﬁned in the situation calculus, a logical language for
reasoning about action and change. Details are in [Reiter, 2001].

Approach
Overview + Contributions
Search Control
Domain Definition+Goal
Domain Definition+Goal
Objective: enable search control in state-of-the-art planners.
Approach: compile search control into domain definition.
Fritz, Baier, McIlraith: ConGolog, Sin Trans 5 / 25

Approach
Overview + Contributions
HTNs∗
ConGolog Basic Action Theories (BAT)
ConGolog with restrictions
Golog w/o procedures
PDDL
Objective: enable search control in state-of-the-art planners.
Approach: compile search control into domain deﬁnition.
Add procedures and concurrency: ConGolog → BATs.
Identify a subset of ConGolog that can be compiled to PDDL.
Can compile ∗simple HTNs into classical planning.
Fritz, Baier, McIlraith: ConGolog, Sin Trans 5 / 25

Planning with Golog-like Procedural Control
Compile Golog into PDDL using -PNFA, using additional book-keeping
actions.
Golog in any PDDL Planner (ICAPS-07)
Golog program
while φ do [if ψ then a else b endif] endwhile
⇓
PNFA for the control
q1 q2
q3 q4
q5 q6
q7 q8
test(φ) test(ψ)
test(¬ψ)
a
b
noop
noop
test(¬φ)
noop
⇒
Automaton’s dynamic in PDDL
New predicate: (aut-state x)
New eﬀects to update for
aut-state
New goal/initial state
Experiments/Applicability shown in our ICAPS-07 paper[Baier, Fritz, M, ICAPS07]

Planning with Golog-like Procedural Control
Main Results:
1 Golog-based procedural control language for Planning
PDDL-based semantics for the language
2 Compilation of Golog-like control into classical planning
Theorem: Translation is correct
Theorem: Translation is polynomial
3 Experimental analysis showing that underconstrained control
provides superior performance
[Baier, Fritz, M, ICAPS07]

Extending the Compilation to ConGolog (Sometimes)
Next we added:
concurrency: being in more than one state simultaneously;
procedures: requires call stack.
Approach:
consider ConGolog program as a Petri Net,
represent this in basic action theory in the situation calculus.
identify a subset of ConGolog that can be compiled to PDDL
[Fritz, Baier, M, KR08] [Christian Fritz, Thesis 2009]

Compilation: Approach
ConGolog:
(if business then bookHotel(Marriott) else bookHotel(DaysInn)
bookFlight)
Petri Net:
1
2
3
4
5
6
7
8
9
10
business
¬business
bookHotel(Marriott)
bookHotel(DaysInn)
bookFlight
noop
noop
spawn join

ConGolog:
bookFlight)
Petri Net:
1
2
3
4
5
6
7
8
9
10
business
¬business
bookHotel(Marriott)
bookHotel(DaysInn)
bookFlight
noop
noop
spawn join

1
2
8
3
5
4
6
7
9
10
business
¬business
bookHotel(Marriott)
bookHotel(DaysInn)
bookFlight
noop
noop
spawn join
Basic Action Theory: (excerpt)

1
2
8
3
5
4
6
7
9
10
business
¬business
bookHotel(Marriott)
bookHotel(DaysInn)
bookFlight
noop
noop
spawn join
Poss(spawn(t, 2, 8), s) ≡ state(t, s) = 1
state(0t, do(spawn(t, 2, 8), s)) = 2

1
2
8
3
5
4
6
7
9
10
business
¬business
bookHotel(Marriott)
bookHotel(DaysInn)
bookFlight
noop
noop
spawn join
Poss(test(t, 2, 3), s) ≡ state(t, s) = 2 ∧ business(s)
state(t, do(test(t, 2, 3), s)) = 3

1
2
8
3
5
4
6
7
9
10
business
¬business
bookHotel(Marriott)
bookHotel(DaysInn)
bookFlight
noop
noop
spawn join
Poss(test(t, 2, 3), s) ≡ state(t, s) = 2 ∧ business(s)
state(t, do(test(t, 2, 3), s)) = 3
Poss(bookFlight(t), s) ≡ state(t, s) = 8 ∧ ΠbookFlight(s)
state(t, do(bookFlight(t), s)) = 9 ← state(t, s) = 8

Compilation: Theorems
Theorem (Correctness)
omitted.
Theorem (Succinct)
The new theory is of size O(n2
), where n is the size of the program.

Compilation: Challenges
Procedures:
New fluent: stack(i, r, s): stack of “return addresses”.
Program variables:
New fluent map(v, x, s): “variable v has value x”.
Concurrency:
Actions get additional thread argument.
New actions spawn and join.
Prioritized concurrency:
New fluent Prio(t2, t2, s): “thread t1 has priority over t2”.
Synchronized if’s and while’s.

Previous Experimental Results
Storage Domain: (log scale)
0.01
0.1
1
10
100
1000
4 6 8 10 12 14 16 18 20 22 24
seconds
testcase
original
dumb

HTNs → ConGolog
unordered: Move2(p1, p2, s, d)
Move1(p1, s, d) Move1(p2, s, d)
→ proc Move2(p1, p2, s, d) : Move1(p1, s, d) Move1(p2, s, d)
ordered: Move1(p, s, d)
load(p) drive(s, d) unload(p)
→ proc Move1(p, s, d) : load(p); drive(s, d); unload(p)
Several methods m1, . . . , mn for decomposing a task Task:
→ proc Task : m1| . . . |mn

Recap
AI planning can be leveraged to improve the generate:
plans with LTL Temporally Extended Goals
plans/Golog programs LTL Temporally Extended Preferences
Plans with Golog-like Procedural Control
...and other things not discussed.
We took a reformulation approach:
Its output is in most cases usable with a wide range of planners
It is composable
Sheds light on the construction of new heuristics
It is applicable to other types of reasoning

Recap (continued)
Formally analyzed the properties of translations/algorithms:
Theorems on the correctness of translations,
Size bounds,
Conditions for optimality,
...
Showed signiﬁcant experimental advantages of the approach
Orders-of-magnitude over state of the art
Superior/competitive with state of the art

Recap (continued)
Contribution: A method for compiling ConGolog search control into
domain definitions (e.g. Basic Action Theories, PDDL).
Merits:
Planning:
Rich search control in any state-of-the-art planner:
ConGolog (with restrictions),
Simple HTNs.
KR:
Makes ConGolog first-order definable.
Avoids need for program reification.
Facilitates automated reasoning about programs.

Take Home Message
Many tasks that involve reasoning about dynamical systems
can be cast as non-classical planning problems and solved
eﬀectively using state-of-the-art planning techniques.
A key enabler for at least 101 of these problems is the
restatement of the objective in terms of LTL, Regular
Expressions (Golog) and/or Automata.

Related Work
LTL/Control Knowledge
LTL Search Control for Planning (Bacchus & Kabanza, AIJ 2000)
Compiling limited subset of LTL to PDDL (Rintanen 2000)
Compiling propositional LTL to PDDL (Cresswell & Codington, ECAI
2004)
LTL Search Control for Planning (Doherty et al., 2000)
LTL Preferences (Son & Pontelli, LPNMR 2004, TPLP 2006)
Extension of PDDL to incorporate LTL (Gerevini, Haslum, Long, et al
2005 & AIJ 2009)
Precondition Control (Gabaldon IJCAI 2003, KR 2004)
Finite LTL + Regular Expressions (De Giacomo & Vardi, IJCAI 2013)
...

Related Work (cont.)
PDDL3 Planners
HPlanP (Baier, Bacchus, M, 2006, AIJ 2009)
MIPS-XXL (Edelkamp et al, 2006)
MIPS-BDD (Edelkamp, 2006)
SGPlan (Hsu et al., 2007)
HPlan-P (Baier, Bacchus, M, 2006, AIJ 2009)
LPRPG-P - (Coles & Coles ICAPS 2011, & Benton ICAPS 2012)
... ?
Planning with Golog Search Control
Planning with Golog (Klassen, Eyerich, Lakemeyer & Nebel, IJCAI
2007)
First-order characterization of Golog and ConGolog (Lin, KR 2014)
Nebel & Lakemeyer & Roeger work on Golog and planning (various)
...

My Personal Journey in Academia
Hon BSc (Pure & Applied Math - Prob & Stats) [U.Ottawa]
In my last 2 years of ugrad, worked for my Stats prof, at the
computer help centre, and as a CS Teaching Assistant
Worked developing software for the oil & gas sector (1.5 yrs)
MSc (Computer Science) [U.Waterloo]
Worked for an applied research lab in Canada (3 years)
PhD (Computer Science) [U.Toronto], continuing p-t work at lab
Postdoc [joint Xerox PARC & Stanford]
(Senior) Research Scientist [Stanford]
Associate Professor → Professor → Vice-Chair → Professor
[U.Toronto]
...and married with 2 children along the way!

Questions?
[Baier2010] [Fritz2009] [Baier and McIlraith2006] [Baier et al.2009]
[Sohrabi et al.2009] [Albarghouthi et al.2009] [Baier et al.2007]
[Sohrabi et al.2006] [Sohrabi et al.2010] [Baier and McIlraith2008]
[Razavi et al.2014] [Razavi et al.2011] [McIlraith et al.2001]
[Bienvenu et al.2011] [Baier et al.2008] [Pnueli1977] [Reiter2001]
[Sohrabi and McIlraith2010] [Bylander1994]

References I
Aws Albarghouthi, Jorge A. Baier, and Sheila A. McIlraith.
On the use of planning technology for verification.
In Proceedings of the Validation and Verification of Planning and Sched. Systems Workshop (VVPS), Thessaloniki, Greece, 2009.
J. Baier and S. McIlraith.
Planning with first-order temporally extended goals using heuristic search.
In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI06), pages 788–795, Boston, MA, July 2006.
Jorge A. Baier and Sheila A. McIlraith.
Planning with preferences.
Artificial Intelligence Magazine, 29(4):25–36, 2008.
Jorge A. Baier, Christian Fritz, and Sheila A. McIlraith.
Exploiting procedural domain control knowledge in state-of-the-art planners.
In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS), pages 26–33, Providence,
Rhode Island, USA, September 22 - 26 2007.
Jorge A. Baier, Christian Fritz, Meghyn Bienvenu, and Sheila McIlraith.
Beyond classical planning: Procedural control knowledge and preferences in state-of-the-art planners.
In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI), Nectar Track, pages 1509–1512, Chicago, Illinois,
USA, July 13–17 2008.
Jorge A. Baier, Fahiem Bacchus, and Sheila A. McIlraith.
A heuristic search approach to planning with temporally extended preferences.
Artificial Intelligence, 173(5-6):593–618, 2009.
Jorge A. Baier.
Effective Search Techniques for Non-Classical Planning via Reformulation.
Ph.D. in Computer Science, University of Toronto, 2010.

References II
Meghyn Bienvenu, Christian Fritz, and Sheila A. McIlraith.
Specifying and computing preferred plans.
Artificial Intelligence, 175(7–8):1308–1345, 2011.
Tom Bylander.
The computational complexity of propositional STRIPS planning.
Artificial Intelligence, 69(1-2):165–204, 1994.
Christian Fritz.
Monitoring the Generation and Execution of Optimal Plans.
PhD thesis, University of Toronto, April 2009.
Sheila A. McIlraith, Tran Cao Son, and Honglei Zeng.
Semantic web services.
IEEE Intelligent Systems. Special Issue on the Semantic Web, 16(2):46–53, March/April 2001.
Amir Pnueli.
The temporal logic of programs.
In Proceedings of the 18th Annual Symposium on Foundations of Computer Science (FOCS), SFCS ’77, pages 46–57, 1977.
Niloofar Razavi, Azadeh Farzan, and Sheila A. McIlraith.
Predicting atomicity violations in concurrent programs via planning.
In Proceedings of the ICAPS-11 Workshop on Verification and Validation of Planning and Scheduling Systems (VVPS), 2011.
Niloofar Razavi, Azadeh Farzan, and Sheila A. McIlraith.
Generating effective tests for concurrent programs via AI automated planning techniques.
International Journal on Software Tools for Technology Transfer (STTT) (STTT), 16(1):49–65, 2014.
Raymond Reiter.
Knowledge in Action: Logical Foundations for Specifying and Implementing Dynamical Systems.
MIT Press, Cambridge, MA, 2001.

References III
Shirin Sohrabi and Sheila A. McIlraith.
Preference-based web service composition: A middle ground between execution and search.
In Proceedings of the 9th International Semantic Web Conference (ISWC-10), pages 713–729, Shanghai, China, November 2010.
Shirin Sohrabi, Natasha Prokoshyna, and Sheila. A. McIlraith.
Web service composition via generic procedures and customizing user preferences.
In Proceedings of the 5th International Semantic Web Conference (ISWC), pages 597–611, Athens, Georgia, 2006.
Shirin Sohrabi, Jorge Baier, and Sheila A. McIlraith.
Htn planning with preferences.
In Proceedings of the 21st International Joint Conference on Artiﬁcial Intelligence (IJCAI), pages 1790–1797, Pasadena,
California, 2009.
Shirin Sohrabi, Jorge Baier, and Sheila A. McIlraith.
Diagnosis as planning revisited.
In Proceedings of the 12th International Conference on Knowledge Representation and Reasoning (KR), Toronto, Canada, 2010.
To appear.

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