TY  - JOUR
A1  - Gebser, Martin
A1  - Kaminski, Roland
A1  - Kaufmann, Benjamin
A1  - Schaub, Torsten H.
T1  - Multi-shot ASP solving with clingo
JF  - Theory and practice of logic programming
N2  - We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo accomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end, clingo offers a new application programming interface that is conveniently accessible via scripting languages. By strictly separating logic and control, clingo also abolishes the need for dedicated systems for incremental and reactive reasoning, like iclingo and oclingo, respectively, and its flexibility goes well beyond the advanced yet still rigid solving processes of the latter.
Y1  - 2018
U6  - https://doi.org/10.1017/S1471068418000054
SN  - 1471-0684
SN  - 1475-3081
VL  - 19
IS  - 1
SP  - 27
EP  - 82
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - GEN
A1  - Razzaq, Misbah
A1  - Kaminski, Roland
A1  - Romero, Javier
A1  - Schaub, Torsten H.
A1  - Bourdon, Jeremie
A1  - Guziolowski, Carito
T1  - Computing diverse boolean networks from phosphoproteomic time series data
T2  - Computational Methods in Systems Biology
N2  - Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.
KW  - Diverse solution enumeration
KW  - Answer set programming
KW  - Boolean Networks
KW  - Model checking
KW  - Time series data
Y1  - 2018
SN  - 978-3-319-99429-1
SN  - 978-3-319-99428-4
U6  - https://doi.org/10.1007/978-3-319-99429-1_4
SN  - 0302-9743
SN  - 1611-3349
VL  - 11095
SP  - 59
EP  - 74
PB  - Springer
CY  - Berlin
ER  -