We propose a framework for probability aggregation based on propositional probability logic. Unlike conventional judgment aggregation, which focuses on static rationality, our model addresses dynamic rationality by ensuring that collective beliefs update consistently with new information. We show that any consensus-compatible and independent aggregation rule on a non-nested agenda is necessarily linear. Furthermore, we provide sufficient conditions for a fair learning process, where individuals initially agree on a specified subset of propositions known as the common ground, and new information is restricted to this shared foundation. This guarantees that updating individual judgments via Bayesian conditioning--whether performed before or after aggregation--yields the same collective belief. A distinctive feature of our framework is its treatment of sequential decision-making, which allows new information to be incorporated progressively through multiple stages while maintaining the established common ground. We illustrate our findings with a running example in a political scenario concerning healthcare and immigration policies.

Discovering a suitable neural network architecture for modeling complex dynamical systems poses a formidable challenge, often involving extensive trial and error and navigation through a high-dimensional hyper-parameter space. In this paper, we discuss a systematic approach to constructing neural architectures for modeling a subclass of dynamical systems, namely, Linear Time-Invariant (LTI) systems. We use a variant of continuous-time neural networks in which the output of each neuron evolves continuously as a solution of a first-order or second-order Ordinary Differential Equation (ODE). Instead of deriving the network architecture and parameters from data, we propose a gradient-free algorithm to compute sparse architecture and network parameters directly from the given LTI system, leveraging its properties. We bring forth a novel neural architecture paradigm featuring horizontal hidden layers and provide insights into why employing conventional neural architectures with vertical hidden layers may not be favorable. We also provide an upper bound on the numerical errors of our neural networks. Finally, we demonstrate the high accuracy of our constructed networks on three numerical examples.

The current state-of-the-art in multi-objective optimization assumes either a given utility function, learns a utility function interactively or tries to determine the complete Pareto front, requiring a post elicitation of the preferred result. However, result elicitation in real world problems is often based on implicit and explicit expert knowledge, making it difficult to define a utility function, whereas interactive learning or post elicitation requires repeated and expensive expert involvement. To mitigate this, we learn a utility function offline, using expert knowledge by means of preference learning. In contrast to other works, we do not only use (pairwise) result preferences, but also coarse information about the utility function space. This enables us to improve the utility function estimate, especially when using very few results. Additionally, we model the occurring uncertainties in the utility function learning task and propagate them through the whole optimization chain. Our method to learn a utility function eliminates the need of repeated expert involvement while still leading to high-quality results. We show the sample efficiency and quality gains of the proposed method in 4 domains, especially in cases where the surrogate utility function is not able to exactly capture the true expert utility function. We also show that to obtain good results, it is important to consider the induced uncertainties and analyze the effect of biased samples, which is a common problem in real world domains.

Kentucky-based Aviation Safety Resources is developing ballistic parachutes for use in aircraft ranging from 60 lbs to 12,000 lbs. The Air Force's Agility Prime program awarded a phase I small business technology transfer (STTR) research contract to Jump Aero and Caltech to create an electronic parachute powered by machine learning that would allow the pilot to recalibrate the flight controller in midair in the event of damage, the company announced on April 7. "The electronic parachute is the name for the concept of implementing an adaptive/machine-learned control routine that would be impractical to certify for the traditional controller for use only in an emergency recovery mode -- something that would be switched on by the pilot if there is reason to believe that the baseline flight controller is not properly controlling the aircraft (if, for example, the aircraft has been damaged in midair)," Carl Dietrich, founder and president of Jump Aero Incorporated, told Avionics International. This technology was previously difficult to certify because of the need for deterministic proof of safety within these complex systems. The research was sparked when the Federal Aviation Administration certified an autonomous landing function for use in emergency situations which created a path for the possible certification of electronic parachute technology, according to Jump Aero. The machine-learned neural network can be trained with non-linear behaviors that occur in an aircraft in the presence of substantial failures such those generated by a bird strike, Dietrich said.

Aerospace company Terrafugia is working on the concept of a flying electric car Called TF-X, the vehicle is designed to be capable of vertical take-offs and landings The goal is to make personal aviation accessible to a broader segment of the population The company estimates a period of 8-12 years before it's able to develop TF-X The company estimates a period of 8-12 years before it's able to develop TF-X Point taken, but perhaps now, as our childhood dreams move slowly closer to reality, we should also start pondering this: if a flying car was here today, in the real world and not in the realm of science fiction, would we feel comfortable controlling it safely while cruising thousands of feet up in the air? Would we possess the technical skills required to even get it off the ground, let alone land it without a scratch? Before you dash to the door and sprint to your nearest pilot school to sign up for flight lessons, take a moment to meet Carl Dietrich, the chief executive and co-founder of aerospace company Terrafugia. Dietrich and his team are working to bring consumers closer to the prospect of a practical flying car, envisioning a vehicle that does not require its operator to be a trained pilot. Thus, Boston -based Terrafugia announced last May it had started working on the concept of TF-X, a four-seat, plug-in hybrid electric car that can do vertical take-offs and landings.

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.

The Mind Doesn't Work That Way: Fodor dubs the synthesis of computationalism, we've got; indeed, the only one like the wrong paradigm for studying However, doesn't work for abductive inferences" types is innate), massive modularity we will have to add something radically (p. Fodor doesn't that the frame problem is why the part in a knowledge base antecedently think we were created, of course; instead of the human mind responsible for deemed to be irrelevant to the inference. Fodor defines irrelevant information, globality rather than by gradual, small transitions, the frame problem as the problem of (pp. Consider just one case the latter being the hallmark of "[h]ow to make abductive inferences from research on analogy: Who would classical adaptationism). This of the atom, but it was relevant.