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Dialogue in Hierarchical Learning of Concept using Prototypes and Counterexamples

Dutta S., Wasilewski P.

Fundamenta Informaticae

2018

Vol. 162, nr 1

17--36

This paper is an attempt to introduce a notion of dialogue among different agents, e.g. a user or situation descriptor and an information provider or machine, to better understand a user’s need of information. When a user describes a concept through some keywords first the system needs to choose the relevant attributes for initiating the search. Regarding this, a series of dialogues may help the system to better understand the user’s language of expresssion. After fixing the relevant attributes, the system needs to look at its repertoire of positive and negative cases of different concepts characterized with respect to this set of attributes. Then through a step-by-step process of matching and comparing similarity of user’s described situation with those positive and negative cases of a concept, incorporating several layers of dialogue, the system may provide information closer to the user’s need. The target of this paper is to develop a theoretical model, incorporating the above features, for characterizing one’s concern of concept based on dialogue and similarity based reasoning.

Coping with strong fairness

Latvala T., Heljanko K.

Fundamenta Informaticae

2000

Vol. 43, Nr 1-4

175-193

We consider the verification of linear temporal logic (LTL) properties of Petri nets, where the transitions can have both weak and strong fairness constraints. Allowing the transitions to have weak or strong fairness constraints simplifies the modeling of systems in many cases. We use the automata theoretic approach to model checking. To cope with the strong fairness constraints efficiently we employ Streett automata where appropriate. We present memory efficient algorithms for both the emptiness checking and counterexample generation problems for Streett automata.