TY - GEN
T1 - Problem solving using process algebra considered insightful.
AU - Groote, J.F.
AU - de Vink, E.P.
PY - 2017
Y1 - 2017
N2 - Process algebras with data, such as LOTOS, PSF, FDR, and mCRL2, are very suitable to model and analyse combinatorial problems. Contrary to more traditional mathematics, many of these problems can very directly be formulated in process algebra. Using a wide range of techniques, such as behavioural reductions, model checking, and visualisation, the problems can subsequently be easily solved. With the advent of probabilistic process algebras this also extends to problems where probabilities play a role. In this paper we model and analyse a number of very well-known – yet tricky – problems and show the elegance of behavioural analysis.
AB - Process algebras with data, such as LOTOS, PSF, FDR, and mCRL2, are very suitable to model and analyse combinatorial problems. Contrary to more traditional mathematics, many of these problems can very directly be formulated in process algebra. Using a wide range of techniques, such as behavioural reductions, model checking, and visualisation, the problems can subsequently be easily solved. With the advent of probabilistic process algebras this also extends to problems where probabilities play a role. In this paper we model and analyse a number of very well-known – yet tricky – problems and show the elegance of behavioural analysis.
UR - http://www.scopus.com/inward/record.url?scp=85032660078&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68270-9_3
DO - 10.1007/978-3-319-68270-9_3
M3 - Conference contribution
SN - 978-3-319-68269-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 48
EP - 63
BT - ModelEd, TestEd, TrustEd
A2 - Katoen, Joost-Pieter
A2 - Langerak, Rom
A2 - Rensink, Arend
PB - Springer
CY - Cham
ER -