Our framework, HARMONIC (Human-AI Robotic Team Member Operating with Natural Intelligence and Communication, Figure 1), is an implemented dual-control cognitive robotic architecture featuring distinct layers of strategic reasoning and tactical, skill-level control [20]. This approach advances the hybrid control systems and architectures reviewed by Dennis et al. [21] and contrasts with DIARC's [22], [23] integration strategy, which embeds the strategic layer within the tactical layer to support concurrent operation. The strategic layer of HARMONIC adapts a mature cognitive architecture, OntoAgent [24], [25], [17] for high-level reasoning, leveraging explicit, structured knowledge representations that can be inspected, verified, and incre-mentally expanded.

Shapes of cognition is a new conceptual paradigm for the computational cognitive modeling of Language - Endowed Intelligent Agents (LEIAs) . S hapes are remembered constellations of sensory, linguistic, conceptual, episodic, and procedural knowledge that allow agents to cut through the complexity of real life the same way as people do: by expecting things to be typical, recognizing patterns, acting by habit, reasoning by analogy, satisficing, and generally minimizing cognitive load to the degree situations permit . Atypical outcomes are treated using shapes - based recovery method s, such as learning on the fly, asking a human partner for help, or seeking an actionable, even if imperfect, situational understanding . Although shapes is an umbrella term, it is not vague: shapes - based modeling involves particular objectives, hypotheses, modeling strategies, knowledge bases, and actual models of wide - ranging phenomena, all implemented within a particular cognitive architecture . Such s pecificity is needed both to vet the our hypotheses and to achieve our practical aims of building useful agent systems that are explainable, extensible, and worthy of our trust, even in critical domains . However, a lthough the LEIA example of shapes - based modeling is specific, the principles can be applied more broadly, giving new life to knowledge - based and hybrid AI .

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 agents. Metacognition is a necessary component of all of these processes. In this paper, we briefly a) introduce content-centric computational cognitive (C4) modeling for next-generation AI agents; b) review the long history of developing C4 agents at RPI's LEIA (Language-Endowed Intelligent Agents) Lab; c) discuss our current work on extending LEIAs' cognitive capabilities to cognitive robotic applications developed using a neuro symbolic processing model; and d) sketch plans for future developments in this paradigm that aim to overcome underappreciated limitations of currently popular, LLM-driven methods in AI.

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 because they are usually black boxes. The explainable AI (XAI) movement hedges this problem by redefining "explanation". The human-centered explainable AI (HCXAI) movement identifies the explanation-oriented needs of users but can't fulfill them because of its commitment to machine learning. In order to achieve the kinds of explanations needed by real people operating in critical domains, we must rethink how to approach AI. We describe a hybrid approach to developing cognitive agents that uses a knowledge-based infrastructure supplemented by data obtained through machine learning when applicable. These agents will serve as assistants to humans who will bear ultimate responsibility for the decisions and actions of the human-robot team. We illustrate the explanatory potential of such agents using the under-the-hood panels of a demonstration system in which a team of simulated robots collaborate on a search task assigned by a human.

We present HARMONIC, a framework for implementing cognitive robots that transforms general-purpose robots into trusted teammates capable of complex decision-making, natural communication and human-level explanation. The framework supports interoperability between a strategic (cognitive) layer for high-level decision-making and a tactical (robot) layer for low-level control and execution. We describe the core features of the framework and our initial implementation, in which HARMONIC was deployed on a simulated UGV and drone involved in a multi-robot search and retrieval task.

Whether computers can actually "think" and "feel" is a question that has long fascinated society. Alan M. Turing introduced a test for gauging machine intelligence as early as 1950. Movies such as 2001: A Space Odyssey and Star Wars have only served to fuel these thoughts, but while the concept was once confined to science fiction, it is rapidly emerging as a serious topic of discussion. In a few cases, the dialog has become so convincing that people have deemed machines sentient. A recent example involves former Google data scientist Blake Lemoine, who published human-to-machine discussions with an AI system called LaMDA.a

This paper details how OntoAgents, language-endowed intelligent agents developed in the OntoAgent framework, assess their confidence in understanding language inputs. It presents scoring heuristics for the following subtasks of natural language understanding: lexical disambiguation and the establishment of semantic dependencies; reference resolution; nominal compounding; the treatment of fragments; and the interpretation of indirect speech acts. The scoring of confidence in individual linguistic subtasks is a prerequisite for computing the overall confidence in the understanding of an utterance. This, in turn, is a prerequisite for the agent's deciding how to act upon that level of understanding.

Developing cognitive agents with human-level natural language understanding (NLU) capabilities requires modeling human cognition because natural, unedited utterances regularly contain ambiguities, ellipses, production errors, implicatures, and many other types of complexities. Moreover, cognitive agents must be nimble in the face of incomplete interpretations since even people do not perfectly understand every aspect of every utterance they hear. So, once an agent has reached the best interpretation it can, it must determine how to proceed – be that acting upon the new information directly, remembering an incomplete interpretation and waiting to see what happens next, seeking out information to fill in the blanks, or asking its interlocutor for clarification. The reasoning needed to support NLU extends far beyond language itself, including, non-exhaustively, the agent’s understanding of its own plans and goals; its dynamic modeling of its interlocutor’s knowledge, plans, and goals, all guided by a theory of mind; its recognition of diverse aspects human behavior, such as affect, cooperative behavior, and the effects of cognitive biases; and its integration of linguistic interpretations with its interpretations of other perceptive inputs, such as simulated vision and non-linguistic audition. Considering all of these needs, it seems hardly possible that fundamental NLU will ever be achieved through the kinds of knowledge-lean text-string manipulation being pursued by the mainstream natural language processing (NLP) community. Instead, it requires a holistic approach to cognitive modeling of the type we are pursuing in a paradigm called OntoAgent.

This is an area where statistics-and MLbased that cognitive system developers currently address systems can be symbiotic with cognitive systems: the and methodological preferences that they, by and former can provide advanced computation frameworks large, share. For some of the issues, the consensus is while the latter can provide content-related not entirely universal, which is to be expected for a insights into the choice of the inventory of features to group of active developers. Still, the general points of consensus should help to characterize the overall be used in making decisions.

This paper details how OntoAgents, language-endowed intelligent agents developed in the OntoAgent framework, assess their confidence in understanding language inputs. It presents scoring heuristics for the following subtasks of natural language understanding: lexical disambiguation and the establishment of semantic dependencies; reference resolution; nominal compounding; the treatment of fragments; and the interpretation of indirect speech acts. The scoring of confidence in individual linguistic subtasks is a prerequisite for computing the overall confidence in the understanding of an utterance. This, in turn, is a prerequisite for the agent’s deciding how to act upon that level of understanding.