3 citations · 6 across the 10 of their papers we have counts for
10 papers
Why Cognitive Robotics Matters: Lessons from OntoAgent and LLM Deployment in HARMONIC for Safety-Critical Robot Teaming
Sanjay Oruganti, Sergei Nirenburg, Marjorie McShane +5
Deploying embodied AI agents in the physical world demands cognitive capabilities for long-horizon planning that execute reliably, deterministically, and transparently. We present…
Shapes of Cognition for Computational Cognitive Modeling
Marjorie McShane, Sergei Nirenburg, Sanjay Oruganti +1
Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language-Endowed Intelligent Agents (LEIAs). Shapes are remembered constellations of se…
HARMONIC: A Content-Centric Cognitive Robotic Architecture
Sanjay Oruganti, Sergei Nirenburg, Marjorie McShane +6
This paper introduces HARMONIC, a cognitive-robotic architecture designed for robots in human-robotic teams. HARMONIC supports semantic perception interpretation, human-like decisi…
Metacognition in Content-Centric Computational Cognitive C4 Modeling
Sergei Nirenburg, Marjorie McShane, Sanjay Oruganti
For AI agents to emulate human behavior, they must be able to perceive, meaningfully interpret, store, and use large amounts of information about the world, themselves, and other a…
Explaining Explaining
Sergei Nirenburg, Marjorie McShane, Kenneth W. Goodman +1
Explanation is key to people having confidence in high-stakes AI systems. However, machine-learning-based systems -- which account for almost all current AI -- can't explain becaus…
HARMONIC: A Framework for Explanatory Cognitive Robots
Sanjay Oruganti, Sergei Nirenburg, Marjorie McShane +3
We present HARMONIC, a framework for implementing cognitive robots that transforms general-purpose robots into trusted teammates capable of complex decision-making, natural communi…
HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams
Sanjay Oruganti, Sergei Nirenburg, Marjorie McShane +7
This paper describes HARMONIC, a cognitive-robotic architecture that integrates the OntoAgent cognitive framework with general-purpose robot control systems applied to human-robot…
Automating Knowledge Acquisition for Content-Centric Cognitive Agents Using LLMs
Sanjay Oruganti, Sergei Nirenburg, Jesse English +1
The paper describes a system that uses large language model (LLM) technology to support the automatic learning of new entries in an intelligent agent's semantic lexicon. The proces…
Knowledge Engineering in the Long Game of Artificial Intelligence: The Case of Speech Acts
Marjorie McShane, Jesse English, Sergei Nirenburg
This paper describes principles and practices of knowledge engineering that enable the development of holistic language-endowed intelligent agents that can function across domains…
Language Generation for Broad-Coverage, Explainable Cognitive Systems
Marjorie McShane, Ivan Leon
This paper describes recent progress on natural language generation (NLG) for language-endowed intelligent agents (LEIAs) developed within the OntoAgent cognitive architecture. The…