Rule-Based Reasoning


How the brain switches between different sets of rules

MIT News

For example, imagine you're driving on a highway at 65 miles per hour. When you exit onto a local street, you realize that the situation has changed and you need to slow down. When we move between different contexts like this, our brain holds multiple sets of rules in mind so that it can switch to the appropriate one when necessary. These neural representations of task rules are maintained in the prefrontal cortex, the part of the brain responsible for planning action. A new study from MIT has found that a region of the thalamus is key to the process of switching between the rules required for different contexts.


Green energy subsidies fuel rise of Northern Ireland mega-farms

Guardian Energy

Green energy subsidies are fuelling the rise of poultry mega-farms across Northern Ireland, with owners accused of contaminating sensitive habitats with emissions from chicken faeces. An alliance of agri-food companies enlisted the support of Northern Ireland politicians to unlock an estimated £800m in subsidies for contractors. This has paved the way for industry expansion at the expense of the environment, according to an investigation by the not-for-profit journalism group SourceMaterial. Moy Park, a food processing company that supplies chicken to some of Britain's largest supermarkets, including Waitrose and Tesco, helped mastermind the plan to turn Northern Ireland into a poultry plantation, according to the investigation, which was published this week. Documents show that Moy Park and other companies successfully lobbied Northern Ireland officials and ministers to adjust the region's agri-business rules in their favour, despite the new policy producing a "nitrogen-soaked environment", according to conservationist Catherine Bertrand.


Stream Reasoning in Temporal Datalog

arXiv.org Artificial Intelligence

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications).


What AI is - and what it is not

#artificialintelligence

What's more, even AIs based on mechanisms inspired by human biology, such as neural networks, have only a distant relationship with biological neurons in the brain. NN are examples more of the importance of reinforcement and self-organisation of controller networks than any similarity with biology. The first, naive, approach to AI is to think that it is necessary to create a synthetic human, or a synthetic brain to produce cognition: in fact, cognition does not need to be anthropomorphic at all. Second attempt at a definition: "The ability of a machine to achieve performance equal to or better than certain human cognitive processes." This definition is based on the final outcome, without presupposing imitation of biological mechanisms.


What Is Artificial Intelligence?

#artificialintelligence

AI is a large topic, and there is no single agreed definition of what it involves. But there seems to be more agreement than disagreement. Broadly speaking, AI is an umbrella term for the field in computer science dedicated to making machines simulate different aspects of human intelligence, including learning, decision-making and pattern recognition. Some of the most striking applications, in fields like speech recognition and computer vision, are things people take for granted when assessing human intelligence but have been beyond the limits of computers until relatively recently. The term "artificial intelligence" was coined in 1956 by mathematics professor John McCarthy, who wrote, The study is to proceed on the basis of the conjecture that every aspect of learning and any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.


MCA-based Rule Mining Enables Interpretable Inference in Clinical Psychiatry

arXiv.org Machine Learning

Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine. Interpretable ML models for clinical healthcare can serve not only as sources of predictions and estimates, but also as discovery tools for clinicians and researchers to reveal new knowledge from the data. High dimensionality of patient information (e.g., phenotype, genotype, and medical history), lack of objective measurements, and the heterogeneity in patient populations often create significant challenges in developing interpretable machine learning models for clinical psychiatry in practice. In this paper we take a step towards the development of such interpretable models. First, by developing a novel categorical rule mining method based on Multivariate Correspondence Analysis (MCA) capable of handling datasets with large numbers of feature categories, and second, by applying this method to build a transdiagnostic Bayesian Rule List model to screen for neuropsychiatric disorders using Consortium for Neuropsychiatric Phenomics dataset. We show that our method is not only at least 100 times faster than state-of-the-art rule mining techniques for datasets with 50 features, but also provides interpretability and comparable prediction accuracy across several benchmark datasets.


The Window Validity Problem in Rule-Based Stream Reasoning

AAAI Conferences

Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time.


DARPA to Grant $2B to AI Projects Over Next Five Years - AI Trends

#artificialintelligence

DARPA stands for "Defense Advanced Research Projects Agency," but while defense is good and all, what DARPA is really into is that P, for projects. The agency is focused on the development of breakthrough technology, and its sights are focused on the enormous potential of artificial intelligence. Its funding for AI projects is huge by any measure, and available to applicants far beyond the traditional defense community. As a 60th birthday present for itself, DARPA launched the AI Next campaign this past September, announcing a $2 billion investment applied to AI in a variety areas over a period of five years -- or about $400 million a year, says Brian Pierce, Director of the Information Innovation Office at DARPA. Anyone can participate in DARPA-funded programs by responding to an invitation for proposals on fbo.gov.


On the k-Boundedness for Existential Rules

arXiv.org Artificial Intelligence

The chase is a fundamental tool for existential rules. Several chase variants are known, which differ on how they handle redundancies possibly caused by the introduction of nulls. Given a chase variant, the halting problem takes as input a set of existential rules and asks if this set of rules ensures the termination of the chase for any factbase. It is well-known that this problem is undecidable for all known chase variants. The related problem of boundedness asks if a given set of existential rules is bounded, i.e., whether there is a predefined upper bound on the number of (breadth-first) steps of the chase, independently from any factbase. This problem is already undecidable in the specific case of datalog rules. However, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound is unknown. Hence, in this paper, we investigate the decidability of the k-boundedness problem, which asks whether a given set of rules is bounded by an integer k. We prove that k-boundedness is decidable for three chase variants, namely the oblivious, semi-oblivious and restricted chase.


Data models for service failure prediction in supply-chain networks

arXiv.org Machine Learning

Abstract--We aim to predict and explain service failures in supply-chain networks, more precisely among last-mile pickup and delivery services to customers. We analyze a dataset of 500,000 services using (1) supervised classification with Random Forests, and (2) Association Rules. Our classifier reaches an average sensitivity of 0.7 and an average specificity of 0.7 for the 5 studied types of failure. Association Rules reassert the importance of confirmation calls to prevent failures due to customers not at home, show the importance of the time window size, slack time, and geographical location of the customer for the other failure types, and highlight the effect of the retailer company on several failure types. To reduce the occurrence of service failures, our data models could be coupled to optimizers, or used to define countermeasures to be taken by human dispatchers. Service failures are pervasive in supply-chain networks, with important consequences on their cost-efficiency and customer experience. We aim at predicting and explaining the cause of such failures, focusing on the last-mile pickup and delivery of items at customer locations. Such services are planned by optimizers solving some variations of the Vehicle-Routing Problem, in our case the Pickup and Delivery Problem with Time Windows (PDPTW [1]).