Readings in model-based diagnosis
Readings in model-based diagnosis
Diagnosis of large active systems
Artificial Intelligence
On Communicating Finite-State Machines
Journal of the ACM (JACM)
Diagnosis of discrete-event systems from uncertain temporal observations
Artificial Intelligence
Incremental construction of minimal acyclic finite-state automata
Computational Linguistics - Special issue on finite-state methods in NLP
An efficient incremental DFA minimization algorithm
Natural Language Engineering
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Incremental processing of temporal observations in Model-Based Reasoning
AI Communications - Model-Based Systems
Incremental Determinization of Finite Automata in Model-Based Diagnosis of Active Systems
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Incremental construction of minimal acyclic finite state automata and transducers
FSMNLP '09 Proceedings of the International Workshop on Finite State Methods in Natural Language Processing
Model-based monitoring of dynamic systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Integrating model-based monitoring and diagnosis of complex dynamic systems
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
A bridged diagnostic method for the monitoring of polymorphic discrete-event systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Monitoring of Active Systems With Stratified Uncertain Observations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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A non-functional requirement for model-based diagnosis of active systems is efficient determinization of acyclic automata. In literature, determinization of finite automata is performed by the Subset Construction algorithm SC: given a nondeterministic automaton N, an equivalent deterministic automaton D is generated, with each state in D being a subset of states in N. However, SC is conceived for monolithic determinization, when N is completely specified. By contrast, monitoring-based diagnosis of active systems requires incremental determinization. We consider the Incremental Determinization Problem for finite acyclic automata: after extending the acyclic automaton N to N' by ΔN by new transitions and, possibly, new states, we require N' to be determinized into D' based on D and ΔN. Although this problem can be naively solved by applying SC to N' thereby disregarding both D and ΔN, this solution is bound to lead to poor performances, as it does not exploit the incremental nature of N'. Therefore, an incremental algorithm is proposed, called ISCA, which extends D into D' based on ΔN, rather than starting from scratch the determinization of N'. ISCA is a general-purpose algorithm. Evidence from experiments indicates that, in time, ISCA is significantly more efficient than SC in solving incremental determinization problems.