mlsanityData to 7 Oct 2026

Towards Cognitive Bots: Architectural Research Challenges

arXiv 2305.17308 ↗First posted 26 May 2023cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Software bots operating in multiple virtual digital platforms must understand the platforms' affordances and behave like human users. Platform affordances or features differ from one application platform to another or through a life cycle, requiring such bots to be adaptable. Moreover, bots in such platforms could cooperate with humans or other software agents for work or to learn specific behavior patterns. However, present-day bots, particularly chatbots, other than language processing and prediction, are far from reaching a human user's behavior level within complex business information systems. They lack the cognitive capabilities to sense and act in such virtual environments, rendering their development a challenge to artificial general intelligence research. In this study, we problematize and investigate assumptions in conceptualizing software bot architecture by directing attention to significant architectural research challenges in developing cognitive bots endowed with complex behavior for operation on information systems. As an outlook, we propose alternate architectural assumptions to consider in future bot design and bot development frameworks.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 7 of its 55 references that are in the map. First posted on arXiv 26 May 2023.

Builds on

7 of 55 references.

References · 7

  1. 2023Language Is Not All You Need: Aligning Perception with Language Modelscited by 543
  2. 2018Learning to Navigate the Webcited by 60
  3. 2022A Rubric for Human-like Agents and NeuroAIcited by 8
  4. 2017Toward a Formal Model of Cognitive Synergycited by 5
  5. 2022Governance of Autonomous Agents on the Web: Challenges and Opportunitiescited by 5
  6. 2023Signifiers as a First-class Abstraction in Hypermedia Multi-Agent Systemscited by 4
  7. 2020A Novel Multi-Agent System for Complex Scheduling Problemscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.

5 papers.