The Israeli military has said it is carrying out air strikes on Hezbollah targets in Tyre in southern Lebanon, after ordering the evacuation of the entire city. The military told residents that it was compelled to act forcefully in Tyre because the Iran-backed armed group was violating a US-brokered ceasefire that began five weeks ago. Earlier on Wednesday, Lebanese media reported a wave of Israeli strikes across the south and the eastern Bekaa Valley, with four people killed in the towns of Choukine and Nabatieh. Hezbollah, which has itself accused Israel of breaching the ceasefire, said it was battling Israeli troops north of the Litani river, about 30km (19 miles) from the border. It came a day after Israel's prime minister announced an expansion of its ground operation following Hezbollah drone attacks on troops occupying part of southern Lebanon and on civilians in northern Israel.

In Formula One, teams compete to develop their cars and achieve the highest possible finishing position in each race. During a race, however, teams are unable to alter the car, so they must improve their cars' finishing positions via race strategy, i.e. optimising their selection of which tyre compounds to put on the car and when to do so. In this work, we introduce a reinforcement learning model, RSRL (Race Strategy Reinforcement Learning), to control race strategies in simulations, offering a faster alternative to the industry standard of hard-coded and Monte Carlo-based race strategies. Controlling cars with a pace equating to an expected finishing position of P5.5 (where P1 represents first place and P20 is last place), RSRL achieves an average finishing position of P5.33 on our test race, the 2023 Bahrain Grand Prix, outperforming the best baseline of P5.63. We then demonstrate, in a generalisability study, how performance for one track or multiple tracks can be prioritised via training. Further, we supplement model predictions with feature importance, decision tree-based surrogate models, and decision tree counterfactuals towards improving user trust in the model. Finally, we provide illustrations which exemplify our approach in real-world situations, drawing parallels between simulations and reality.

Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In this study, we evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks, focusing on three key aspects: feasibility, optimality, and generalizability. Through empirical evaluations on constraint-heavy tasks (e.g., $\textit{Barman}$, $\textit{Tyreworld}$) and spatially complex environments (e.g., $\textit{Termes}$, $\textit{Floortile}$), we highlight o1-preview's strengths in self-evaluation and constraint-following, while also identifying bottlenecks in decision-making and memory management, particularly in tasks requiring robust spatial reasoning. Our results reveal that o1-preview outperforms GPT-4 in adhering to task constraints and managing state transitions in structured environments. However, the model often generates suboptimal solutions with redundant actions and struggles to generalize effectively in spatially complex tasks. This pilot study provides foundational insights into the planning limitations of LLMs, offering key directions for future research on improving memory management, decision-making, and generalization in LLM-based planning. Code available at https://github.com/VITA-Group/o1-planning.

Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, for example that two agents in the domain can execute an action simultaneously if postconditions of each do not interfere with preconditions of the other. A human expert can decompose a goal into largely independent constituent parts and assign each agent to one of these subgoals to take advantage of simultaneous actions for faster execution of plan steps, each using only single agent planning. By contrast, large language models (LLMs) used for directly inferring plan steps do not guarantee execution success, but do leverage commonsense reasoning to assemble action sequences. We combine the strengths of classical planning and LLMs by approximating human intuitions for two-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success. Additionally, we find that LLM-based approximations of subgoals can achieve similar multi-agent execution steps than those specified by human experts. Website and resources at https://glamor-usc.github.io/twostep

From a well-timed recyclable PPE glove to a wheel-based device to cut "invisible" pollution from tyres, 20 groundbreaking designs by students across the world are in the running to be named on Thursday as the international winner of the annual James Dyson award. The prestigious accolade brings with it a £30,000 cash prize – and gives winners a chance to turn their innovation into a commercial product with real-world impact. One in five previous winners have gone on to commercialise a wide range of exotic inventions, including bionic arms, origami-style clothing and bio-reactive food labels. For this year's award, a record 1,798 entries have been submitted – two-thirds more than last year – with a majority addressing urgent global issues such as climate change, sustainability, medicine and healthcare. This year's entries opened in March amid the deepening coronavirus pandemic.

The way we drive is changing. Globally, trends like urbanization, carpooling, and eventually, autonomous vehicles will mean that the demands we place on our vehicles will change, too. To meet this challenge, the design and function of every vehicular part must be re-imagined to fit these needs, and this includes the tire. How Goodyear Is Using Data, Artificial Intelligence And Digital Twins To Create The Tyres Of The ... [ ] Future Goodyear is a world-leading supplier of tires for cars as well as every type of commercial, industrial and agricultural vehicle. The US manufacturer has built its reputation by leading the development of tire technology since the late 19th century.

It's not just new track-ready hypercars and bruising German super saloons that've made their debut at the 2018 Geneva Motor Show. Pirelli has a new tyre. In other news, Greggs has a new pasty. Normally we wouldn't bring you updates on new tyres, but we'll make an exception for Pirelli's'Cyber Car' technology. You've heard of phones, thermostats and even entire cars connected to the Internet of Things.

Gas-guzzling cars may contribute to global warming, but designs for a new tyre made from moss may soon help them mitigate their impact. Goodyear has announced a concept wheel covering that will convert carbon dioxide (CO2) into oxygen, using the power of photosynthesis. Oxygene tyres will channel moisture from the road to the plants, which absorbs water up to 26 times their own weight. This will be used to keep the vegetation alive, allowing it to clean the air whilst in use - using only the sun as an energy source. The energy produced by the process will power on-board artificial intelligence and electronics, which will communicate with other vehicles at the speed of light. Car tyres may soon be able to tackle global warming, and its all thanks to moss.

Let us understand this with a simple analogy. Driving a car has always excited me, since my childhood I've imagined of owning a car and steering endlessly on the highways. To fulfill my dream, as soon as I attained the age of 18 I enrolled myself into a driving school. So excited to hold the steering in my hand! I was careful to note all the instructions conveyed by the instructor.

Goodyear's new concept tyre was unveiled in Geneva. Goodyear presented its Eagle 360 Urban concept tyre during the Geneva International Motor Show. The difference to the spherical concept tyre from last year is that the new model can not only be manoeuvred in all directions, but is also equipped with artificial intelligence. The tyre should be able to make decisions in order to adapt to and interact with the driving conditions. Together with a bionic outer skin and a morphing tread, the Eagle 360 Urban should be able to deploy the information it collects directly into the driving situation.