Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysis

This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.Comment: AAAI 2023 Fall Symposiu

Conformance Checking Based on Multi-Perspective Declarative Process Models

Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behaviors provided in the form of a process model. The majority of these approaches require the input process model to be procedural (e.g., a Petri net). However, in turbulent environments, characterized by high variability, the process behavior is less stable and predictable. In these environments, procedural process models are less suitable to describe a business process. Declarative specifications, working in an open world assumption, allow the modeler to express several possible execution paths as a compact set of constraints. Any process execution that does not contradict these constraints is allowed. One of the open challenges in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. In this paper, we close this gap by providing a framework for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented in three real life case studies

Cognitive approaches in L2 pragmatics research

This chapter reviews L2 pragmatics research informed by cognitive SLA theories. Following R. Ellis’s (2008) classification, the chapter first introduces two theories — the two-dimensional model, and the skill acquisition theory or ACT-R, which focus on the mental representation of L2 knowledge and then reviews empirical evidence in L2 pragmatics under these theories. The chapter then shifts to the Noticing Hypothesis that concerns cognitive processes that can lead to changes in mental representation of L2 knowledge. Suggestions for future research under each of the three cognitive SLA theorizations are provided. This chapter ends with a call for more studies that examine the mental representation of L2 pragmatic knowledge and how that mental representation develops under the influence of various cognitive processes

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

SCAFFOLDING IN TEACHING WRITING NEWS ITEM TEXT, ITS BENEFITS AND CHALLENGES

The research aimed at investigating types of scaffolding, the ways a teacher provides scaffolding, and benefits and challenges of scaffolding in teaching writing News Item text in tenth graders in a senior high school in Bandung. This research employed a qualitative research design, embracing characteristic of a case study. The data were obtained from non-participant classroom observation for six meetings, and one interview. The data collected from classroom observation and interview were transcribed and then analyzed by using framework of types of scaffolding (e.g Roehler and Cantlon (1997), Hammond (2001), Gibbons, (2002), and Walqui (2006)). The findings revealed that seven types of scaffolding were provided by the teacher   during teaching writing News Item text. The most intensive scaffolding was given in the Modeling stage, while scaffolding was removed in the Independent Writing stage. Regarding the benefits and challenges of providing scaffolding, this research found out the benefits of scaffolding such as to connect students’ prior knowledge with a new concept, to engage students, to minimize the level confusion of students, and to build students‟ self-confidence. Meanwhile, the challenges of providing scaffolding are the number of students in the classroom, time constraints, and demands on teacher

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment