State-Merging DFA Induction Algorithms with Mandatory Merge Constraints
نویسندگان
چکیده
Standard state-merging DFA induction algorithms, such as RPNI or Blue-Fringe, aim at inferring a regular language from positive and negative strings. In particular, the negative information prevents merging incompatible states: merging those states would lead to produce an inconsistent DFA. Whenever available, domain knowledge can also be used to extend the set of incompatible states. We introduce here mandatory merge constraints, which form the logical counterpart to the usual incompatibility constraints. We show how state-merging algorithms can benefit from these new constraints. Experiments following the Abbadingo contest protocol illustrate the interest of using mandatory merge constraints. As a side effect, this paper also points out an interesting property of statemerging techniques: they can be extended to take any pair of DFAs as inputs rather than simple strings.
منابع مشابه
Learning DFA: evolution versus evidence driven state merging
Learning Deterministic Finite Automata (DFA) is a hard task that has been much studied within machine learning and evolutionary computation research. This paper presents a new method for evolving DFAs, where only the transition matrix is evolved, and the state labels are chosen to optimize the fit between final states and training set labels. This new procedure reduces the size and in particula...
متن کاملGradual probabilistic DFA learning with caching for conversational agents
SUMMARY This paper proposes a method of reducing the cost of gradually constructing task-oriented conversational agents with FSMs by collecting example dialogues. Probabilistic-DFA learning algorithms with the state merging method are available for the FSM-based dialogue model. However, these algorithms must learn the whole model again as often as the example data increase. We proposed a learni...
متن کاملSemantics-Aware Versioning Challenge: Merging Sequence Diagrams along with State Machine Diagrams
In multi-view modeling languages like UML, models contain several diagrams, each of which focusing on a specific aspect of the system. However, when the diagrams are combined, they give a coherent description of all static and dynamic aspects of the system. Diagrams may then extend each other or add constraints to other diagrams. Considering this additional information improves model versioning...
متن کاملHow Considering Incompatible State Mergings May Reduce the DFA Induction Search Tree
A simple and eeective method for DFA induction from positive and negative samples is the state merging method. The corresponding search space may be tree-structured, considering two subspaces for a given pair of states: the subspace where states are merged and the subspace where states remain diierent. Choosing diierent pairs leads to diierent sizes of space, due to state mergings dependencies....
متن کاملEvolutionary learning of Ontology Merging Algorithms
Ontology merging is an activity of merging two or more source ontology’s to get single coherent ontology for extended knowledge. Literature suggests that in past, several ontology merging algorithms have been proposed by researchers for semantic information retrieval. The key idea associated with all those algorithms was how to merge ontology’s semantically which gives the best possible results...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008