AIhub October 2026 Monthly Roundup: Neuro-Symbolic Planning, Time Series Forecasting, and Home Robots

AIhub monthly digest: October 2026 – neurosymbolic planning, time-series forecasting, and robots for the home

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we chat to AI pioneer, Michael Littman, learn about time-series forecasting for clinical data, investigate evolutionary game theory for cybersecurity, and get the scoop on neuromorphic computing.

From teaching machines to teaching people: an interview with Michael Littman

In the latest interview in our series meeting AI pioneers, Michael Littman reflects on the evolution of reinforcement learning, the power of science communication, and how generative AI is changing the way we teach and learn.

Machine learning for clinical time-series forecasting

Mayra Elwes is a PhD student developing machine learning methods for clinical time-series data. She is working on practical strategies to evaluate methods in a more contextualised way and on improving existing domain adaptation methods specific to time-series forecasting. We caught up with Mayra to find out more.

Cybersecurity and evolutionary game theory

Adeela Bashir is working at the intersection of cybersecurity, evolutionary game theory, and AI safety. We heard from Adeela about her PhD, in which she is focussed on the question: how can we understand and mitigate cyber threats when the adversaries are adaptive? In other words, can AI defend itself when the attackers are evolving too?

Competing in RoboCup@Home and winning the 2026 best paper award

Raphael Memmesheimer has long been competing in the RoboCup@Home league winning the RoboCup@Home competition five times. He and his colleagues took home the best paper award at RoboCup 2026 for their work Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation. We chatted up with Raphael to find out more about this research, his history with RoboCup, and the challenges of the @Home competition.

Neurosymbolic planning using natural language communication for cooperative autonomous vehicles

In this blog post, Neel P. Bhatt, Po-han Li, Kushagra Gupta, Ufuk Topcu and David Fridovich-Keil write about their research on neurosymbolic planning using natural language communication for cooperative autonomous vehicles. This work was nominated for the AAMAS 2026 best paper award.

Vision language models and the physical world

At ICML 2026, Dezhi Luo, Yijiang Li, Maijunxian Wang, Tianwei Zhao, Bingyang Wang, Siheng Wang, Pinyuan Feng, Pooyan Rahmanzadehgervi, Ziqiao Ma and Hokin Deng presented their work Vision Language Models Cannot Reason About Physical Transformation. In this interview, William Yijiang Li tells us more about the research, the method the team used, and the controlled experiments they carried out.

Rebuilding the brain with neuromorphic computing

What can we learn from the brain about building better computers? Oliver Rhodes discusses how neuromorphic computing draws on the brain’s architecture to develop new approaches to more efficient information processing.

Credit fairness: lending resources without losing out

In their paper presented at IJCAI-ECAI 2026, Seyed Majid Zahedi and Rupert Freeman investigate repeated allocation of shared resources among agents. They introduce credit fairness, a property that strengthens sharing incentives. Read their blog post to find out more.

AAAI/SIGAI Doctoral Consortium interview collection

Each year, a small group of PhD students are chosen to participate in the AAAI/SIGAI Doctoral Consortium. This initiative provides an opportunity for the students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. During 2026, we met with 15 of these students to find out more about their research. We’ve collated all of these interviews here.

AI, ethics and society in Sweden

The ninth AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) took place in Malmö, Sweden from 12-14 October 2026. The programme included keynote talks, a panel, discussion sessions, paper presentations, and posters. We cast an eye over social media to find out what the participants got up to. Look out for blog posts from some of the presenters who will be writing about work they brought to the conference.

2027 ACM SIGAI Autonomous Agents Research Award open for nominations

Nominations are solicited for the 2027 ACM SIGAI Autonomous Agents Research Award. This award is made for excellence in research in the area of autonomous agents. It is intended to recognize researchers whose current work is an important influence on the field. Find out how to nominate here.

AAAI Fellows interview series

Lucy Smith

is Senior Managing Editor for AIhub.

Lucy Smith

is Senior Managing Editor for AIhub.

From teaching machines to teaching people: an interview with Michael Littman

Ella Scallan

29 Oct 2026

AI Pioneer Michael Littman reflects on the evolution of reinforcement learning, the power of science communication, and how generative AI is changing the way we teach and learn.

From lab to life: Why translating AI advances to the real world is a major challenge

The Conversation

28 Oct 2026

Systems that perform well in controlled lab settings often fall short when deployed in homes, in hospitals or on city streets.

Researchers get AI ‘drunk’ to expose new cyber security risks in chatbots

University of New South Wales

27 Oct 2026

Fine-tuning large language models using drunken text makes them more likely to leak secrets and answer harmful questions.

AI is creating jobs as well as erasing them – but how rewarding are they?

The Conversation

26 Oct 2026

One area of increased employment is in people “cleaning up” after LLMs, or quality controlling them.

How AI changes human decision-making

Linköping University

23 Oct 2026

AI can identify patterns that people miss and bring new information into decision-making. However, this does not automatically lead to better decisions.

Competing in RoboCup@Home and winning the 2026 best paper award: an interview with Raphael Memmesheimer

Lucy Smith

22 Oct 2026

Hear more about Raphael's research and his involvement in the RoboCup@Home league.

AI is distorting archaeology and flattening Indigenous Knowledge

Pursuit, University of Melbourne

21 Oct 2026

While AI can help archaeologists, it can also spread pseudoscientific claims and false histories, stripping Indigenous Knowledge of context, consent and community control.

ACM SIGAI Autonomous Agents Award 2027 open for nominations

ACM SIGAI

20 Oct 2026

Nominations are solicited for the 2027 ACM SIGAI Autonomous Agents Research Award.

Association for the Understanding of Artificial Intelligence