Beyond subgoaling: A dynamic knowledge generation framework for creative problem solving in cognitive architectures

Abstract

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures.

Analysis of the Relationship Between Concept Mastery and Problem-Solving Skills of Pre-Service Biology Teachers in Human Physiology Courses

“…The impact is that the solutions to the problems given are not logical and cannot be applied in everyday life. Giving questions in the form of real problems in the student environment will train critical thinking skills so that they can produce a solution that can be applied in everyday life (Lieto, et al, 2019). Problem solving activities, especially in the aspect of considering solutions, require students to produce systematic and logical thinking (Chua, et al, 2014).…”

Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework

“…In similar work, Wicaksono and Sammut (Wicaksono & Sheh, 2017;Wicaksono & Sammut, 2020) integrate planning within the CREATIVE architecture, to enable a robot to craft novel tools through 3D printing, as opposed to using available objects. Lastly, in the cognitive architecture of SOAR, Lieto et al (Lieto, Perrone, Pozzato, & Chiodino, 2019) show that concept representation in a knowledge base can be used as a means for "subgoal" resolution (or plan repair) within planning.…”

“…The connections and their corresponding strengths can be hand-coded, or learned over time. In Lieto et al (Lieto et al, 2019), CPS is performed by exploring conceptual blending in a semantic network of concepts, represented using a non-monotonic logic system which allows for concept composition. This representation accounts a measure for "common-sense applicability" on its semantic concepts.…”

“…Thus the novel concepts are a combination of the semantic features in the initial conceptual space. Closely related is the work of Lieto et al (Lieto et al, 2019), where object substitution is found through combining existing object "concepts". Solution objects are selected from a set of candidate objects which are evaluated based on their rank of object similarity to the object being substituted.…”

A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification

“…In this logic, "typical" properties can be directly specified by means of a "typicality" operator T enriching the underlying Description Logic (from now on, DL for short), and a TBox can contain inclusions of the form T(C) D to represent that 1 Other works have already shown how such logic can be used to model complex cognitive phenomena [25], creative problem solving [26,22] and to build intelligent applications in the field of computational creativity [24]. Alternative approaches to the problem of commonsense conceptual combination have been recently discussed in [13], [21], [10].…”