Building Smarter, Easily Reconfigurable, Dynamic and Robust Voice Assistants Leveraging Macro-Program-augmented QAKBs
Creators: name: Lutalo, Joseph Willrich
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 460299
subject: Artificial intelligence not elsewhere classified
classificationCode: 400701
subject: Assistive robots and technology

description: Past work concerning the design and implementation of voice-operated personal assistants that are easy to configure and apply without directly worrying about the sophistication of leveraging Large Language Models or Neural Networks has been conducted at Makerere University via the use of intelligent parsing of user voice queries against simple plain-text Question-Answer Knowledge Bases (QAKBs). This work was part of the VOSA project, and was successfully presented at SE2024 in Copenhagen. However, one glitch in this Artificial Assistant model design is that the accuracy and relevance of the answers provided by the robot are so tightly coupled to the literal phrasing and expression of answers (and alternative answers) in the underlying manually prepared QA entries that define/compose a knowledge model against which the bot can correctly respond to questions about a particular domain. In reality though, such an approach can become too limiting; the authors of a QAKB need to know/anticipate all/most possible ways a user might pose a question to the robot in order to correctly match an entry in the KB; handling of answers that need to contain dynamic information (such as date and time, location, prices, weather and such) isn't easily catered for by the flat, literal and static JSON-formatted QAKB structures used in VOSA-like assistants much as they retain the advantage of being simpler for humans to reason about or tweak than most current alternatives using human-opaque structures such as use of matrices of probabilities in Hidden Markov [Knowledge] Models (HMKMs) or use of thousands if not millions or billions of weights and activation functions in Neural Network Models (NNMs). With the recent emergence of a feature-complete general-purpose Text Processing language, TEA, which, by its lightweight nature could allow for easy integration into existing or future intelligent systems, an idea for how VOSA-like Artificial Assistants could be further enhanced in terms of their cognitive processing power and efficiency is by leveraging smarter QAKB models that also allow for some runtime/just-in-time reprogramming/generation of answers to suit user-context and perhaps even add multi-turn QA Processing that is currently missing in VOSA but which is somewhat necessary given the state-of-the-art in assistant chatbot technologies across the industry. So, how might this better QAKB technology be realized? One idea is to add a TEA program processor inside of the VOSA client engine/architecture, and then, to allow for designers or authors of VOSA-targeting QAKBs to extend their QA-entries with specially crafted, domain relevant mini-programs or perhaps QAKB-wide QA-preprocessing Macro Programs either of which are written using the succinct, terse but powerful text processing language of TEA, the Transforming Executable Alphabet. This then would allow for VOSAC to sometimes take a user's query and the active QAKB, and then dynamically process it relative to preprocessing instructions embedded in the QAKB, and thus compile new or better answers to user queries on-the-fly and in a manner perhaps more straightforward and more transparent than leveraging pre-trained models or merely falling back to over-the-web queries to a search engine as the VOSA paper had originally proposed. This research shall also usher in a whole new approach to designing and programming Artificial Intelligence personal assistants that's different from approaches leveraging HMKMs, NNMs or just basic QAKBs and/or ontologies. Consider this a research proposal abstract.