=Paper=
{{Paper
|id=Vol-2745/paper7
|storemode=property
|title=Answer Set Programming in Healthcare: Extended Overview
|pdfUrl=https://ceur-ws.org/Vol-2745/paper7.pdf
|volume=Vol-2745
|authors=Mario Alviano,Riccardo Bertolucci,Matteo Cardellini,Carmine Dodaro,Giuseppe Galatà,Muhammad Kamran Khan,Marco Maratea,Marco Mochi,Victoria Morozan,Ivan Porro,Marco Schouten
|dblpUrl=https://dblp.org/rec/conf/aiia/AlvianoBCDGKMMM20
}}
==Answer Set Programming in Healthcare: Extended Overview==
https://ceur-ws.org/Vol-2745/paper7.pdf
Answer Set Programming in Healthcare:
Extended Overview?
Mario Alviano1 , Riccardo Bertolucci2 , Matteo Cardellini2 , Carmine Dodaro1 ,
Giuseppe Galatà3?? , Muhammad Kamran Khan2 , Marco Maratea2 , Marco
Mochi2,3 , Victoria Morozan2 , Ivan Porro3 , and Marco Schouten2
1
DEMACS, University of Calabria, Rende, Italy,
{alviano, dodaro}@mat.unical.it
2
DIBRIS, University of Genova, Genova, Italy
riccardo.bertolucci@unige.it, matteo.cardellini@edu.unige.it,
muhammad.kamrankhan@unige.it,marco.mochi@edu.unige.it,marco.maratea@unige.it,
victoria.morozan@edu.unige.it,marco.shouten@edu.unige.it
3
SurgiQ srl, Italy
{name.surname}@surgiq.com
Abstract. The ASP methodology has been recognized to be a viable
solution to many practical applications, including scheduling problems,
and in particular in the healthcare domain, of high interest in this period
given the COVID-19 pandemic, where ASP proved to be an effective
solution to some interesting problems.
In this paper we present an overview of scheduling problems in the health-
care domain that have been successfully solved via ASP in the last two
years. Starting from two ”basic” problems, i.e., referred to the schedul-
ing of operating rooms in presence of scarce resources (e.g., ICU beds)
and the Nurse Scheduling problem, we show how we have improved such
solutions, and mention further problems we have dealt with, including
the Chemotherapy Scheduling problem. On top of all problems, we are
working on providing explainability features, of utmost importance in
this field, that for the moment are being instantiated on a single prob-
lem.
1 Introduction
Answer Set Programming (ASP) [36, 37] has been recognized to be a viable
methodology for solving many practical applications, e.g., Artificial Intelligence [9],
Bioinformatics [27, 43], Hydroinformatics [29], and Databases [48]; more recently,
ASP has been applied to solve industrial applications [3, 24, 25]. Indeed, the sim-
ple syntax [12] and the intuitive semantics [37], combined with the availability of
robust implementations (see, e.g. [5, 30, 47, 46]) put forward by the ASP Compe-
tition series (see, e.g. [13, 32, 33, 14, 35, 34, 45, 31]), make ASP an ideal candidate
for addressing combinatorial problems that naturally arise in these contexts.
?
Copyright 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
??
Corresponding author.
Recently, ASP has been also applied to solving scheduling problems, and in
particular problems in the healthcare domain, of high interest in this period
given the COVID-19 pandemic.
In this paper we present an overview of scheduling problems in the health-
care domain that have been successfully solved via ASP in the last two years
(mainly) in Genova, and from this year in the context of the Scheduling and
Resource Management topic of the CLAIRE COVID-19 Task Force4 . The start-
ing point are two basic problems that we have overviewed in [22], namely the
Nurse Scheduling problem [10, 16] and the Operating Room Scheduling (ORS)
problem [2, 8, 44, 49] . The first refers to the organization of the working shifts of
nurses in an hospital unit, while the second refers to the scheduling of operating
rooms in presence of scarce resources (e.g., ICU beds). We describe both the
improvements we have designed and implemented on these two basic problems,
as well as other scheduling problems we have solved, i.e., the Chemotherapy
Scheduling problem. The description of the problems takes into account real re-
quirements for small-medium sized Italian Hospitals or private institutes, and
the resulting ASP encodings have been tested on instances with realistic sizes
and parameters. On top of all problems, we are working on providing explain-
ability features, of utmost importance in this field, that for the moment are being
instantiated on the ORS problem.
The paper is structured as follows. Section 2 introduces our starting problems,
while Section 3 describes the improvements we made over these problems. Then,
Section 4.2 outlines further problems we have solved, and Section 5 concludes
the paper and envisages directions for future research.
2 Description of the two basic problems
In this section we briefly describe our two starting problems, outlined in [22].
2.1 Nurse scheduling problem
The Nurse Scheduling problem (NSP) consists of generating a schedule of work-
ing and rest days for nurses working in hospital units. The schedule should
determine the shift assignments of nurses for a predetermined window of time,
and must satisfy requirements imposed by the Rules of Procedure of hospitals.
Our NSP definition contains requirements from the International Nurse Roster-
ing Competition [15]. A proper solution to the NSP is crucial to guarantee the
high level of quality of healthcare, to improve the degree of satisfaction of nurses
and the recruitment of qualified personnel.
In particular, three different types of requirements have been considered,
namely hospital, nurses, and balance requirements.
Hospital requirements include the different types of shifts that can be consid-
ered, namely morning (7 A.M. – 2 P.M.), afternoon (2 P.M. – 9 P.M.), and night
4
https://covid19.claire-ai.org/.
(9 P.M. – 7 A.M.). In order to ensure the best assistance program for patients,
each shift is associated with a minimum and a maximum number of nurses that
must be present in the hospital.
Nurses requirements are expressed to guarantee a fair workload between
nurses. Therefore, a limit on the minimum and maximum number of working
hours per year is imposed. Moreover, additional requirements are imposed to
ensure an adequate rest period to each nurse: (a) nurses are legally guaranteed
30 days of paid vacation, (b) the starting time of a shift must be at least 24
hours later than the starting time of the previous shift, and (c) each nurse has
at least two rest days each fourteen days window. In addition, after two consec-
utive working nights there is one special rest day which is not included in the
rest days of (c).
Finally, balance requirements ensure that the number of times a nurse can
be assigned to morning, afternoon and night shifts is fixed.
Solutions for this problem can be found in [1, 26, 6].
2.2 Operating room scheduling problem
In the following we will call a registration each single planned surgery inserted in
the hospital waiting list. A registration is associated to a patient and is charac-
terized by a predicted surgery duration and length of stay (LOS) in the hospital
ward, it is assigned to a specialty (e.g. General Surgery, Orthopedics, etc.) and
has a priority score, which takes into account two different factors: the surgical
procedure urgency and the time already spent in the waiting list. We have clas-
sified each registration according to three different priority categories, namely
P1 , P2 and P3 . The first one gathers either very urgent registrations or the ones
that have been longer in the waiting list; these registrations must be assigned to
the OR schedule. The P2 registrations should be assigned but can be postponed
in case of necessity, while the last category collects the registrations that may
be used to fill any possible hole left in the schedule.
The schedule is organized in a series of OR time blocks, uniquely identified
by the OR id and the time and date when the block is scheduled. The num-
ber and distribution of the OR blocks available for each specialty during the
whole planning period is given by the cyclic timetable of the hospital, referred
to as the Master Surgical Schedule (MSS) and set beforehand by the hospital
management.
The overall goal of our formulation of the ORS problem is to assign the
maximum number of registrations, subject to the following constraints:
• each registration assigned to a OR block must belong to the same specialty
associated to the block,
• each registration must be assigned to a single block and to a bed of the
specialty associated to the block by the MSS for all the LOS,
• the sum of the surgery durations of the registrations assigned to a OR block
must not exceed the length of the block itself,
• the registrations belonging to the priority category P1 must all be assigned,
while the number of unassigned registrations belonging to the other cate-
gories must be minimized, prioritizing the P2 ones.
A first ASP solution for this problem can be found in [21].
3 Improvements to the basic problems
In this section we will describe, in two subsections, the improvements that have
been made, or are currently undertaken, to the problems outlined in Section 2
in the last couple of years.
3.1 Improvements to NSP
Employee scheduling problem. The NSP has been generalized to consider ev-
ery employee type needed to operate an hospital or a clinic, such as kitchen
staff, cleaning staff, nurses and unlicensed assistive personnel. All staff should
be appropriately allocated to provide high quality and efficient health services.
Effective employee scheduling considers factors such as patient needs, staff needs,
organisational needs, the workforce and skills required to deliver services, and
workforce availability.
The automatically generated staff schedule must take into consideration em-
ployees working restrictions, the required skill-mix, safe staffing hours, employees
agreed clinical unavailability and, when feasible, staff preferences and person-
alised working patterns. In particular, according to the constraints we consid-
ered, each employee:
• is entitled to one day off every N days (where N is customizable for each
employee type),
• can only be qualified to perform certain shifts (e.g., only in the morning and
afternoon but not at night),
• cannot work more than two consecutive night shifts,
• after each night shift, he/she can be assigned either another night shift or a
rest,
• can express preferences on the shifts to be assigned (e.g., an operator can be
qualified for all shifts but would prefer to avoid the nights),
• can request holidays, that may be expressed either as preferences, with no
guarantee that the requested dates are fully respected, or as constraints.
In order to cover the situations where the employees of more than one facility
needs to be scheduled, and some of these employees can be moved between differ-
ent facilities depending on their daily needs, our solution supports a two-phase
scheduling procedure. The first phase of scheduling deals with the allocation
of shifts of employees working permanently in a single structure, covering one
month (but several months can be scheduled in sequence). Typically not all
shifts are expected to be covered at this stage. In the second phase, the shifts
left uncovered in the first phase are assigned to the most mobile operators, who
can move from facility to facility as required. For each facility the classic shifts
in the morning, afternoon and night are considered, with some variations based
on the different possible contracts.
Rescheduling procedure for NSP. The Nurse Rescheduling Problem (NRP) ad-
dresses situations where an already computed schedule is not usable because
of sudden absences of some nurses. Nurses are following a previously computed
schedule, and some of them report impossibility to work on some future days, for
example because of health problems or personal issues. The previously computed
schedule has to be changed starting from a future day that must not follow any
reported absence.
The new schedule needs to satisfy all requirements of the NSP with the excep-
tion that any absence due to health problems must not be rescheduled. Addition-
ally, the new schedule minimizes the differences with the previous schedule, and
such a minimization has priority over any other optimality NSP requirements.
A solution for the NRP can be found in Section 6 of [7], where in particular
the situation mentioned earlier is represented with a new atom representing that
a nurse is absent for an interval of days for a given reason.
3.2 Improvements to ORS
Solution to the MSS problem. In Section 2.2 we considered the MSS as set
beforehand by the hospital management, so among the inputs of the problem
and encoding. However, one may want to have the control over it (the hospital
management) or to have the possibility to tune and modify it. For these reasons,
we have defined an encoding for computing the MSS. Starting from the set of
available ORs, the available sessions and their durations, the specialties and a
planning horizon, we have defined an encoding for computing a MSS for that
planning horizon. The ASP encoding devoted to the MSS computation can be
unified to the ORS encoding in a modular way, given that the output (predicate)
of the MSS encoding has been designed for corresponding to the input predicate
of the ORS encoding.
ORS with ward and ICU beds. The ORS problem is extended with the task of
assigning maximum patients to operating rooms, taking into account different
specialties, surgery durations, and the availability of beds for the entire length
of stay (LOS) both in the Intensive Care Unit (ICU) and in the specialty wards.
Given that patients may have priorities, the solution has to find an accommo-
dation for the patients with highest priorities, and then to the other with lower
priorities, if space is still available, at the same time taking into proper account
beds availability.
The overall goal of the ORS with beds management is to assign the maximum
number of registrations to the ORs, taking explicitly into consideration the avail-
ability of beds in the wards and in the ICU. We have ensured that a particular
registration can only be assigned to an OR only if there is an available bed for
the patient in the ward for the entire LOS.
In our model, a patient’s LOS has been subdivided in the following phases:
• a LOS in the ward before surgery: It is possible that a patient is admitted
to the ward a day (or more) before the surgery takes place;
• the LOS after surgery, It considers the duration of patient stay after surgery
which can be further subdivided into the ICU LOS (duration of stay in ICU)
and the following ward LOS (duration of stay in the specialty ward).
Following two requirements related to the beds management are considered:
• ward bed requirement. Here we consider a situation where each specialty
is linked to a ward with a variable number of available beds exclusively
dedicated to the patients associated to that specialty.
• ICU bed requirement. Here we consider ICU as a particular type of ward that
is accessible to patients from any specialty. However, only a small percentage
of patients is expected to need to stay in the ICU.
A solution to this problem can be found in [19].
ORS with PACU beds. Details on the treatment of beds have been explained in
the previous paragraph. In this paragraph we introduce another type of beds we
have analyzed. Similarly as above, we have also extended the ORS encoding in
order to take into account also Post Anesthetic Care Unit (PACU) beds. After
a surgery, (fortunately) not all patients need an ICU bed, but a (vast) majority
need a PACU bed, where they can recover from the anesthetic. Patients that need
a bed go either in PACU or in ICU. These PACU beds are separated to those
of the ward and the ICU, and the characteristic of their usage is that they are
occupied by a patients for a limited (in minutes) amount of time, often related to
a percentage of the surgery duration. The requirements and constraints entailed
by the presence of PACU are similar to those of ICU, and have been added to
the previous encoding in a modular way, while ensuring that a patient may go
in PACU or ICU alternatively.
Rescheduling procedure for ORS. Similarly to the NSP problem, but for slightly
different reasons, it is important to design a rescheduling procedure also in case
of the ORS problem. Also in this case, the rescheduling procedure is applied to
a previously planned schedule, i.e., we start from an already created schedule
that could not be executed fully till the end due to some reasons, e.g., some
patients could not be operated in their assigned slots or the patients may delete
their registration. In such situation all those postponed registrations (or surg-
eries) must be reallocated to one of the next slots in the remaining part of the
original planning period. Once planned, a speciality schedule does not influence
other specialties so it makes sense to reschedule one specialty at a time. Since we
already have the initial schedule for the planning period, we assume that in day
2 (out of a planning period of 5 days) a number of registrations from a specialty
had to be postponed to a next day. In order to insert the postponed registrations
in the new schedule we have to make sure that the start of the schedule leaves
enough available OR time by automatically dropping the necessary registrations
from the old schedule. The encoding for the rescheduling procedure has been
defined starting from the ORS encoding, substituting the old schedule (consid-
ered now as input of the problem) with the new to be computed, and modularly
adding the necessary rules for representing the wanted rescheduling behavior.
4 New problems and explainability
In this section we present, in two separate subsections, a new scheduling problem
we have solved with ASP, and explainability features we are adding to one of
our solutions.
4.1 Chemotherapy Scheduling problem
The Chemotherapy Treatment Scheduling (CTS) problem consists of generating
a calendar of treatment following a chemotherapy regimen for every patient.
We call registration every single treatment inserted on the waiting list. A regis-
tration is associated with a patient and a sequential number, since every patient
will follow a chemotherapy regimen and the sequential number represents the
order to follow. The registration is characterized by the drugs that the patient
needs, the days to wait for the next treatment, i.e., the registration associated
with the same patient and the next sequential number, and has a priority score
with values from 1 to 3.
Each drug has a different time of administration, represented as time slot in our
solution, and dose. Every treatment needs a nurse and a chair; the number of
nurses, chairs, and, the times slot in which the treatments can take place are set
beforehand according to the hospital or clinic availability.
The goal of our solution to the CTS problem is to schedule all registrations
following some constraints:
• each treatment must be separated from the previous treatment of the same
person by the number of days indicated in the registration;
• each registration must be assigned to an available chair and to an available
nurse;
• each patient to which more drugs are assigned in a treatment has to stay in
the same chair during all the time of the treatment;
• the sum of a drug used during a day must not exceed the available quantity
of that drug;
• each patient with a priority 1 must have the first treatment before a specific
day n, such as the one with priority 2 that must have the first treatment
before a specific day n + m and the one with priority 3 must have the first
treatment before the end of the planning days considered.
A solution based on ASP can be found in [20], and it is quite general to as
to possibly include other treatment with relatively low effort, like the radiology
treatment [11, 17, 28].
4.2 Explainability
Explainable Artificial Intelligence (AI) refers to methods and techniques in the
application of AI methodology such that the results of the solution can be under-
stood by humans, also specifically for the medical domain [42]. We are working
on adding explainability features to our ORS solution. As an example, it may
happen that no feasible solutions are available for the given set of inputs. There-
fore, there is the necessity to:
• investigate which elements of the input set led to the unsatisfiability of the
problem (UIE),
• explain in human language these causes in order to make them comprehen-
sible to a large amount of users.
In order to isolate those elements we analyze the registrations in the waiting
list with the highest priority, since the lower priority registrations can not lead
to unsatisfiability, the availability of the beds and the OR time blocks. Once
the UIEs are isolated, it is possible to track the inconsistencies that led to the
unsatisfiability, e.g., if it is found that the issue is due to the number of beds
of a certain speciality we may state that the issue is generated because the
number of available beds in that speciality is too low or the registrations for
that speciality is too high. Once the UIEs and their causes have been identified
it is possible to proceed with the explanation in human language of the causes
of the unsatisfiability. Moreover, we use the gathered data to isolate elements of
interest (EI) that are used as suggestions for the user to remove the UIE; e.g.,
if the issue is due to the lack of beds in a certain speciality, we can give as EI
the list of all the registrations with high priority belonging to that speciality.
Furthermore, it is given to the user the possibility to remove from the input
set any EI making, the user an integral part of the error identification process.
Thus, the user perceive the explanation as more reliable since he took part to
the identification process.
5 Conclusions and future research
In this document we have overviewed the extended and new contributions to
solvoling scheduling problems in the healthcare domain we carried out in the
last couple of years mainly in Genova, by employing the ASP methodology. As a
result, we can say that in all studied problems up to now ASP led to satisfying
solutions, both on the scientific and application view-points.
In the future we would like to continue the effort in the following four direc-
tions: (i) add new problems, e.g., at the moment we are working on planning
and scheduling the pre-surgery phase, (ii) deepen the integration of the solutions
for the various problems in order to be able to solve bigger and more concrete
problems, (iii) confront our solutions to others available in the state of the art,
whenever possible and meaningful, and (iv) turn our ASP encodings into usable
applications, by allowing, e.g., to set parameters and run the encoding via a web
interface, as we have already done for the basic ORS problem [23]. Moreover,
even if we did not give details of the performance of our solution, there are over-
all satisfying from a computational and application viewpoint. Nonetheless, we
plan to work also on this direction by both evaluating more solvers, e.g., WASP
[4], other than Clingo actually used, and employing SAT techniques (e.g., [40,
41, 18]), given the strong relation between ASP and SAT [39, 38].
References
1. Abdennadher, S., Schlenker, H.: Nurse scheduling using constraint logic program-
ming. In: Hendler, J., Subramanian, D. (eds.) Proceedings of the Sixteenth National
Conference on Artificial Intelligence and Eleventh Conference on Innovative Ap-
plications of Artificial Intelligence, July 18-22, 1999, Orlando, Florida, USA. pp.
838–843. AAAI Press / The MIT Press (1999)
2. Abedini, A., Ye, H., Li, W.: Operating Room Planning under Surgery Type and
Priority Constraints. Procedia Manufacturing 5, 15–25 (2016)
3. Abseher, M., Gebser, M., Musliu, N., Schaub, T., Woltran, S.: Shift design with
answer set programming. Fundamenta Informaticae 147(1), 1–25 (2016)
4. Alviano, M., Amendola, G., Dodaro, C., Leone, N., Maratea, M., Ricca, F.: Eval-
uation of disjunctive programs in WASP. In: Balduccini, M., Lierler, Y., Woltran,
S. (eds.) LPNMR. Lecture Notes in Computer Science, vol. 11481, pp. 241–255.
Springer (2019)
5. Alviano, M., Dodaro, C.: Anytime answer set optimization via unsatisfiable core
shrinking. Theory and Practice of Logic Programming 16(5-6), 533–551 (2016)
6. Alviano, M., Dodaro, C., Maratea, M.: An advanced answer set programming
encoding for nurse scheduling. In: AI*IA. LNCS, vol. 10640, pp. 468–482. Springer
(2017)
7. Alviano, M., Dodaro, C., Maratea, M.: Nurse (re)scheduling via answer set pro-
gramming. Intelligenza Artificiale 12(2), 109–124 (2018)
8. Aringhieri, R., Landa, P., Soriano, P., Tànfani, E., Testi, A.: A two level meta-
heuristic for the operating room scheduling and assignment problem. Computers
& Operations Research 54, 21–34 (2015)
9. Balduccini, M., Gelfond, M., Watson, R., Nogueira, M.: The USA-Advisor: A case
study in answer set planning. In: LPNMR. LNCS, vol. 2173, pp. 439–442. Springer
(2001)
10. Burke, E.K., Causmaecker, P.D., Berghe, G.V., Landeghem, H.V.: The state of the
art of nurse rostering. J. Scheduling 7(6), 441–499 (2004)
11. Burke, E.K., Rocha, P.L., Petrovic, S.: An integer linear programming model for
the radiotherapy treatment scheduling problem. CoRR abs/1103.3391 (2011),
http://arxiv.org/abs/1103.3391
12. Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T.,
Leone, N., Maratea, M., Ricca, F., Schaub, T.: ASP-Core-2 input language format.
Theory and Practice of Logic Programming 20(2), 294–309 (2020)
13. Calimeri, F., Gebser, M., Maratea, M., Ricca, F.: The design of the fifth answer
set programming competition. CoRR abs/1405.3710 (2014), http://arxiv.org/
abs/1405.3710
14. Calimeri, F., Gebser, M., Maratea, M., Ricca, F.: Design and results of the Fifth
Answer Set Programming Competition. Artificial Intelligence 231, 151–181 (2016)
15. Ceschia, S., Dang, N., Causmaecker, P.D., Haspeslagh, S., Schaerf, A.: The sec-
ond international nurse rostering competition. Ann. Oper. Res. 274(1-2), 171–186
(2019)
16. Cheang, B., Li, H., Lim, A., Rodrigues, B.: Nurse rostering problems - a biblio-
graphic survey. European Journal of Operational Research 151(3), 447–460 (2003)
17. Conforti, D., Guerriero, F., Guido, R.: Non-block scheduling with priority for ra-
diotherapy treatments. Eur. J. Oper. Res. 201(1), 289–296 (2010)
18. Di Rosa, E., Giunchiglia, E., Maratea, M.: A new approach for solving satisfia-
bility problems with qualitative preferences. In: Ghallab, M., Spyropoulos, C.D.,
Fakotakis, N., Avouris, N.M. (eds.) ECAI. Frontiers in Artificial Intelligence and
Applications, vol. 178, pp. 510–514. IOS Press (2008)
19. Dodaro, C., Galatà, G., Khan, M.K., Maratea, M., Porro, I.: An asp-based solution
for operating room scheduling with beds management. In: RuleML+RR. Lecture
Notes in Computer Science, vol. 11784, pp. 67–81. Springer (2019)
20. Dodaro, C., Galatà, G., Maratea, M., Mochi, M., Porro, I.: Chemotherapy treat-
ment scheduling via answer set programming. In: Calimeri, F., Perri, S., Zumpano,
E. (eds.) Proceedings of the 35th Italian Conference on Computational Logic (CILC
2020). CEUR Workshop Proceedings, vol. 2710, pp. 342–356. CEUR-WS.org (2020)
21. Dodaro, C., Galatà, G., Maratea, M., Porro, I.: Operating room scheduling via an-
swer set programming. In: AI*IA. LNCS, vol. 11298, pp. 445–459. Springer (2018)
22. Dodaro, C., Galatà, G., Maratea, M., Porro, I.: An overview of ASP applications
in the health-care domain. In: Proceedings of the RiCeRcA Workshop co-located
with the 17th International Conference of the Italian Association for Artificial
Intelligence, (RiCeRcA@Ai*iA 2018). CEUR Workshop Proceedings, vol. 2272.
CEUR-WS.org (2018)
23. Dodaro, C., Galatà, G., Maratea, M., Porro, I.: An asp-based framework for oper-
ating room scheduling. Intelligenza Artificiale 13(1), 63–77 (2019)
24. Dodaro, C., Gasteiger, P., Leone, N., Musitsch, B., Ricca, F., Schekotihin, K.: Com-
bining answer set programming and domain heuristics for solving hard industrial
problems (application paper). Theory and Practice of Logic Programming 16(5-6),
653–669 (2016)
25. Dodaro, C., Leone, N., Nardi, B., Ricca, F.: Allotment problem in travel industry:
A solution based on ASP. In: RR. LNCS, vol. 9209, pp. 77–92. Springer (2015)
26. Dodaro, C., Maratea, M.: Nurse scheduling via answer set programming. In: LP-
NMR. LNCS, vol. 10377, pp. 301–307. Springer (2017)
27. Erdem, E., Öztok, U.: Generating explanations for biomedical queries. Theory and
Practice of Logic Programming 15(1), 35–78 (2015)
28. Gatta, R., Vallati, M., Mazzini, N., Kitchin, D.E., Bonisoli, A., Gerevini, A., Valen-
tini, V.: On the efficient allocation of diagnostic activities in modern imaging de-
partments. In: Progress in Artificial Intelligence - 17th Portuguese Conference on
Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015. Pro-
ceedings. pp. 103–109 (2015)
29. Gavanelli, M., Nonato, M., Peano, A.: An ASP approach for the valves positioning
optimization in a water distribution system. Journal of Logic and Computation
25(6), 1351–1369 (2015)
30. Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider,
M.T.: Potassco: The Potsdam answer set solving collection. AI Communications
24(2), 107–124 (2011)
31. Gebser, M., Leone, N., Maratea, M., Perri, S., Ricca, F., Schaub, T.: Evaluation
techniques and systems for answer set programming: a survey. In: Lang, J. (ed.)
Proceedings of the Twenty-Seventh International Joint Conference on Artificial
Intelligence, IJCAI 2018. pp. 5450–5456. ijcai.org (2018)
32. Gebser, M., Maratea, M., Ricca, F.: The Design of the Sixth Answer Set Program-
ming Competition. In: LPNMR. LNCS, vol. 9345, pp. 531–544. Springer (2015)
33. Gebser, M., Maratea, M., Ricca, F.: What’s Hot in the Answer Set Programming
Competition. In: AAAI. pp. 4327–4329. AAAI Press (2016)
34. Gebser, M., Maratea, M., Ricca, F.: The design of the seventh answer set program-
ming competition. In: Balduccini, M., Janhunen, T. (eds.) LPNMR. Lecture Notes
in Computer Science, vol. 10377, pp. 3–9. Springer (2017)
35. Gebser, M., Maratea, M., Ricca, F.: The sixth answer set programming competi-
tion. Journal of Artificial Intelligence Research 60, 41–95 (2017)
36. Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming.
In: Proceedings of the Fifth International Conference and Symposium , Seattle,
Washington, August 15-19, 1988 (2 Volumes). pp. 1070–1080. MIT Press (1988)
37. Gelfond, M., Lifschitz, V.: Classical Negation in Logic Programs and Disjunctive
Databases. New Generation Comput. 9(3/4), 365–386 (1991)
38. Giunchiglia, E., Leone, N., Maratea, M.: On the relation among answer set solvers.
Ann. Math. Artif. Intell. 53(1-4), 169–204 (2008)
39. Giunchiglia, E., Maratea, M.: On the Relation Between Answer Set and SAT Pro-
cedures (or, Between cmodels and smodels). In: ICLP. LNCS, vol. 3668, pp. 37–51.
Springer (2005)
40. Giunchiglia, E., Maratea, M., Tacchella, A.: Dependent and independent variables
in propositional satisfiability. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.)
JELIA. Lecture Notes in Computer Science, vol. 2424, pp. 296–307. Springer (2002)
41. Giunchiglia, E., Maratea, M., Tacchella, A.: (In)Effectiveness of look-ahead tech-
niques in a modern SAT solver. In: Rossi, F. (ed.) CP. Lecture Notes in Computer
Science, vol. 2833, pp. 842–846. Springer (2003)
42. Holzinger, A., Biemann, C., Pattichis, C.S., Kell, D.B.: What do we need to build
explainable AI systems for the medical domain? CoRR abs/1712.09923 (2017),
http://arxiv.org/abs/1712.09923
43. Koponen, L., Oikarinen, E., Janhunen, T., Säilä, L.: Optimizing phylogenetic su-
pertrees using answer set programming. TPLP 15(4-5), 604–619 (2015)
44. Landa, P., Aringhieri, R., Soriano, P., Tànfani, E., Testi, A.: A hybrid optimization
algorithm for surgeries scheduling. Operations Research for Health Care 8, 103–114
(2016)
45. Lierler, Y., Maratea, M., Ricca, F.: Systems, engineering environments, and com-
petitions. AI Magazine 37(3), 45–52 (2016)
46. Maratea, M., Pulina, L., Ricca, F.: A multi-engine approach to answer-set pro-
gramming. Theory and Practice of Logic Programming 14(6), 841–868 (2014)
47. Maratea, M., Ricca, F., Faber, W., Leone, N.: Look-back techniques and heuristics
in DLV: Implementation, evaluation, and comparison to QBF solvers. J. Algo-
rithms 63(1-3), 70–89 (2008)
48. Marileo, M.C., Bertossi, L.E.: The consistency extractor system: Answer set pro-
grams for consistent query answering in databases. Data Knowl. Eng. 69(6), 545–
572 (2010)
49. Molina-Pariente, J.M., Hans, E.W., Framinan, J.M., Gomez-Cia, T.: New heuris-
tics for planning operating rooms. Computers & Industrial Engineering 90, 429–
443 (2015)