Abstract
Our aim is to model the behaviour of a cognitive agent trying to solve a complex problem by dividing it into sub-problems, but failing to solve some of these sub-problems. We use the powerful framework of erotetic search scenarios (ESS) combined with Kleene's strong three-valued logic. ESS, defined on the grounds of Inferential Erotetic Logic, has appeared to be a useful logical tool for modelling cognitive goal-directed processes. Using the logical tools of ESS and the three-valued logic, we will show how an agent could solve the initial problem despite the fact that the sub-problems remain unsolved. Thus our model not only indicates missing information but also specifies the contexts in which the problem-solving process may end in success despite the lack of information. We will also show that this model of problem solving may find use in an analysis of natural language dialogues.
Highlights
We will assume the perspective of an agent trying to solve a compound problem by dividing it into sub-problems
Using the logical machinery briefly described above we model the process of problem-solving with information gaps
In this paper we have used erotetic search scenarios in order to model the behaviour of an agent solving a complex problem
Summary
We will assume the perspective of an agent trying to solve a compound problem by dividing it into sub-problems. We will concern ourselves with situations when the agent is not capable of solving the problem on his/her own, we will assume that collecting information involves a questioning process. – the questioned agent does not want to share his/her knowledge; – information provided by the questioned agent is not suited to our agent’s needs (e.g. the answer provides little or too much information— certain additional processing steps need to be taken). The answer provides little or too much information— certain additional processing steps need to be taken) In each of these cases our agent deals with a problem-solving process with some information gaps involved. Using the logical machinery briefly described above we model the process of problem-solving with information gaps. – the identification of missing information, – the identification of the contexts in which the process of solving the initial problem may end in success despite the lack of information
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