Despite advances in embodied AI, agent reasoning systems still struggle to capture the fundamental conceptual structures that humans naturally use to understand and interact with their environment. To address this, we propose a novel framework that bridges embodied cognition theory and agent systems by leveraging a formal characterization of image schemas, which are defined as recurring patterns of sensorimotor experience that structure human cognition. By customizing LLMs to translate natural language descriptions into formal representations based on these sensorimotor patterns, we will be able to create a neurosymbolic system that grounds the agent's understanding in fundamental conceptual structures. We argue that such an approach enhances both efficiency and interpretability while enabling more intuitive human-agent interactions through shared embodied understanding.

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.

Learning-based solutions are efficient, but are they trustworthy enough to be embedded in a robot cooperating with or assisting humans? In this blogpost, Daniele Meli explores this question, and reviews logic programming as a route to trustworthy autonomous (and cooperative) robotic systems. As part of the 37th AAAI Conference on Artificial Intelligence (AAAI2023), 32 different workshops were held, covering a wide range of topics. We heard from the organisers of four of these workshops, who told us their key takeaways from their respective events. These were split into two articles: 1) #AAAI2023 workshops round-up 1: AI for credible elections, and responsible human-centric AI, and 2) #AAAI2023 workshops round-up 2: health intelligence and privacy-preserving AI. Hosted by the Alan Turing Institute, AI UK is a two-day conference that showcases artificial intelligence and data science research, development, and policy in the UK. This year, the event took place on 21 and 22 March, and we covered the panel discussion session on the role and impact of science journalism. AAAI have updated their publication policy to deal with AI systems: "It is AAAI's policy that any AI system, including Generative Models such as Chat-GPT, BARD, and DALL-E, does not satisfy the criteria for authorship of papers published by AAAI and, as such, also cannot be used as a citable source in papers published by AAAI".

Have you ever watched a mystery movie and noticed a critical link before the characters did? Maybe this critical link even helped you solve the mystery before the end of the story. If so, think about how you made that connection: did you notice something that "just didn't add up?" Maybe, instead, one character's behavior seemed too suspicious from the beginning. Whether you made a logical deduction or made a prediction based on behavioral patterns, you performed link prediction, a common task researchers use on computational graph structures. In our recent survey paper, we focus specifically on methods which operate on graph structures.