Toward a Unified Theory of Immediate Reasoning in Soar

Soar is an architecture for general intelligence that has been proposed as a unified theory of human cognition (UTC) (Newell, 1989) and has been shown to be capable of supporting a wide range of intelligent behavior (Laird, Newell & Rosenbloom, 1987; Steier et al, 1987). Polk & Newell (1988) showed that a Soar theory could account for human data in syllogistic reasoning. In this paper, we begin to generalize this theory into a unified theory of immediate reasoning based on Soar and some assumptions about subjects' representation and knowledge. The theory, embodied in a Soar system (IR-Soar), posits three basic problem spaces (comprehend, test-proposition, and build-proposition) that construct annotated models and extract knowledge from them, learn (via chunking) from experience and use an attention mechanism to guide search. Acquiring task specific knowledge is modeled with the comprehend space, thus reducing the degrees of freedom available to fit data. The theory explains the qualitative phenomena in four immediate reasoning tasks and accounts for an individual's responses in syllogistic reasoning. It represents a first step toward a unified theory of immediate reasoning and moves Soar another step closer to being a unified theory of all of cognition

Sets for foundational representations? A design case study with probability and distributions

Ideas about sets are foundational to our understanding of many knowledge domains. And cognitive science tells us that the representation (notation or visualization) we use to encode the knowledge of a domain substantially determines what we can think and how easily we can reason about that do-main. Therefore, how a representation encodes ideas about sets may sub-stantially determine how readily we can comprehend, solve problems and learn about its domain. So, how should we design representations for knowledge rich domains to ensure that concepts about sets are readily ac-cessible and also effectively integrated with the domain’s other concepts? A case study is presented in which a representation for sets (Set Space Dia-grams) is taken as a foundation for a representation for probability theory (Probability Space Diagrams) and then further extended as a representation for statistical distributions (Distribution Space Diagrams). Together the three representations constitute a unified framework that conceptually inte-grates knowledge across the three domains

Knowledge Distillation Under Ideal Joint Classifier Assumption

Knowledge distillation is a powerful technique to compress large neural networks into smaller, more efficient networks. Softmax regression representation learning is a popular approach that uses a pre-trained teacher network to guide the learning of a smaller student network. While several studies explored the effectiveness of softmax regression representation learning, the underlying mechanism that provides knowledge transfer is not well understood. This paper presents Ideal Joint Classifier Knowledge Distillation (IJCKD), a unified framework that provides a clear and comprehensive understanding of the existing knowledge distillation methods and a theoretical foundation for future research. Using mathematical techniques derived from a theory of domain adaptation, we provide a detailed analysis of the student network's error bound as a function of the teacher. Our framework enables efficient knowledge transfer between teacher and student networks and can be applied to various applications

Quantum Interaction Approach in Cognition, Artificial Intelligence and Robotics

The mathematical formalism of quantum mechanics has been successfully employed in the last years to model situations in which the use of classical structures gives rise to problematical situations, and where typically quantum effects, such as 'contextuality' and 'entanglement', have been recognized. This 'Quantum Interaction Approach' is briefly reviewed in this paper focusing, in particular, on the quantum models that have been elaborated to describe how concepts combine in cognitive science, and on the ensuing identification of a quantum structure in human thought. We point out that these results provide interesting insights toward the development of a unified theory for meaning and knowledge formalization and representation. Then, we analyze the technological aspects and implications of our approach, and a particular attention is devoted to the connections with symbolic artificial intelligence, quantum computation and robotics.Comment: 10 page

A Systematic Knowledge Management Approach Using Object-Oriented Theory in Customer Complaint Management

Research into the effectiveness of customer complaint management has attracted researchers, yet there has been little discussion on customer complaint management in the context of systematic knowledge management approach particularly in the domain of hotel industry. This paper aims to address such gap through the application of object-oriented theory for which the notation of unified modelling language has been adopted for the representation of the concepts, objects, relationships and vocabularies in the domain. The paper used data from forty seven hotel management staff and academics in hospitality management to investigate lessons learned and best practices in customer complaint management and knowledge management. By providing insights into the potential of a knowledge management approach using object oriented theory, this study advances our understanding on how a knowledge management approach can systematically support the management of hotel customer complaints

Fundamental Composite Higgs Dynamics on the Lattice:SU(2) with Two Flavors

In reference [1] a unified description, both at the effective and fundamental Lagrangian level, of models of composite Higgs dynamics was proposed. In the unified framework the Higgs itself can emerge, depending on the way the electroweak symmetry is embedded, either as a pseudo-Goldstone boson or as a massive excitation of the condensate. The most minimal fundamental description consists of an SU(2) gauge theory with two Dirac fermions transforming according to the defining representation of the gauge group. We therefore provide first principle lattice results for the massive spectrum of this theory. We confirm the chiral symmetry breaking phenomenon and determine the lightest spin-one axial and vector masses. The knowledge of the energy scale at which new states will appear at the Large Hadron Collider is of the utmost relevance to guide experimental searches of new physics.Comment: 17 pages, 7 figure

A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows

This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes. Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques. The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base. The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete. After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system. A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling

Developing a Knowledge-based System for Complex Geometrical Product Specification (GPS) Data Manipulation.

Geometrical product specification and verification (GPS) matrix system is a universal tool for expressing geometrical requirements on product design drawings. It benefits product designers through providing detailed description of functional requirements for geometrical products, and through referring to corresponding manufacturing and verification processes. In order to overcome current implementation problems highlighted in this paper, a GPS knowledge base and a corresponding innovative inference mechanism have been researched, which led to the development of an integrated GPS knowledge-based system to facilitate rapid and flexible manufacturing requirements. This paper starts with a brief introduction of GPS, GPS application problems and the project background. It then moves on to demonstrate a unified knowledge acquisition and representation mechanism based on the category theory (CT) with five selected examples of this project. The paper concludes with a discussion on the future works for this projec