Modern software for propositional satisfiability problems gives a powerful automated reasoning toolkit, capable of outputting not only a satisfiable/unsatisfiable signal but also a justification of unsatisfiability in the form of resolution proof (or a more expressive proof), which is commonly used for verification purposes. Empirically, modern SAT solvers produce relatively short proofs, however, there are no inherent guarantees that these proofs cannot be significantly reduced. This paper proposes a novel branch-and-bound algorithm for finding the shortest resolution proofs; to this end, we introduce a layer list representation of proofs that groups clauses by their level of indirection. As we show, this representation breaks all permutational symmetries, thereby improving upon the state-of-the-art symmetry-breaking and informing the design of a novel workflow for proof minimization. In addition to that, we design pruning procedures that reason on proof length lower bound, clause subsumption, and dominance. Our experiments suggest that the proofs from state-of-the-art solvers could be shortened by 30-60% on the instances from SAT Competition 2002 and by 25-50% on small synthetic formulas. When treated as an algorithm for finding the shortest proof, our approach solves twice as many instances as the previous work based on SAT solving and reduces the time to optimality by orders of magnitude for the instances solved by both approaches.

Common crops, like wheat or maize, could be genetically altered to be brightly coloured to make it easier for weeding robots to do their job, suggest researchers. Weeding reduces the need for herbicides, but the artificial intelligence models that power weeding robots can struggle to differentiate between crops and weeds that are a similar shape and colour. To get round this problem, Pedro Correia at the University of Copenhagen in Denmark and his colleagues have suggested that crop genomes could be adapted to express pigments such as anthocyanins, which make blueberries blue, or carotenoids, which make carrots orange. Crops could also be grown to have unusually shaped leaves or to have characteristics that are invisible to the naked eye but detectable by sensors, such as in the infrared spectrum, they say. Correia says AI's struggles with weeding could be exacerbated as wild species are adapted for agriculture to capitalise on their abilities to cope with a changing climate.

Receptor-binding domain-binder designs displayed on yeast. From De novo design of protein interactions with learned surface fingerprints. Reproduced under a CC BY 4.0 licence. In 2019, scientists in the joint School of Engineering and School of Life Sciences Laboratory of Protein Design and Immunoengineering (LPDI) led by Bruno Correia developed MaSIF: a machine learning-driven method for scanning millions of protein surfaces within minutes to analyze their structure and functional properties. The researchers' ultimate goal was to computationally design protein interactions by finding optimal matches between molecules based on their surface chemical and geometric "fingerprints".

This paper describes a social robotic game player that is able to successfully play a team card game called Sueca. The question we will address in this paper is: how can we build a social robot player that is able to balance its ability to play the card game with natural and social behaviours towards its partner and its opponents. The first challenge we faced concerned the development of a competent artificial player for a hidden information game, whose time constraint is the average human decision time. To accomplish this requirement, the Perfect Information Monte Carlo (PIMC) algorithm was used. Further, we have performed an analysis of this algorithm's possible parametrizations for games trees that cannot be fully explored in a reasonable amount of time with a MinMax search.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale SA working, and protect trees for pulp and paper producer Suzano SA. In the future, it's a system that may be put to work in California, where deadly wildfires abound. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale working, and protect trees for pulp and paper producer Suzano. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances. By linking them to artificial intelligence, a small Portugal-based company working with IBM Corp. believes it can help tame the often unpredictable affects of climate change.

On a tower in the Brazilian rain forest, a sentinel scans the horizon for the first signs of fire. They don't blink or take breaks, and guided by artificial intelligence they can tell the difference between a dust cloud, an insect swarm and a plume of smoke that demands quick attention. In Brazil, the devices help keep mining giant Vale SA working, and protect trees for pulp and paper producer Suzano SA. In the future, it's a system that may be put to work in California, where deadly wildfires abound. The equipment includes optical and thermal cameras, as well as spectrometric systems that identify the chemical makeup of substances.

As wildfires become tougher to control, one company is looking to fight the flames with tech. Compta Emerging Business is the winner of this year's Watson Build Competition sponsored by IBM. The Portugal-based company developed a solution that uses its patented spectrometric analysis technology to detect fires automatically within 5 minutes of ignition and within a range of up to 15 kilometers. "We are bringing artificial intelligence to the game so wildfires can be detected at the earliest stage," said Vasco Correia, Director of International Business at Compta. "We can detect very early and we can recommend firefighting measures."