Learning representations that capture rich semantic relationships and accommodate propositional calculus poses a significant challenge. Existing approaches are either contrastive, lacking theoretical guarantees, or fall short in effectively representing the partial orders inherent to rich visual-semantic hierarchies. In this paper, we propose a novel approach for learning visual representations that not only conform to a specified semantic structure but also facilitate probabilistic propositional reasoning. Our approach is based on a new nuclear norm-based loss. We show that its minimum encodes the spectral geometry of the semantics in a subspace lattice, where logical propositions can be represented by projection operators.

Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting (CEF). Our framework combines two formalisms: a) symbolic automata which are used to encode complex event patterns; and b) prediction suffix trees which can provide a succinct probabilistic description of an automaton's behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. Our experimental results demonstrate the benefits, in terms of accuracy, of being able to capture such long-term dependencies. This is achieved by increasing the order of our model beyond what is possible with full-order Markov models that need to perform an exhaustive enumeration of all possible past sequences of a given order. We also discuss extensively how CEF solutions should be best evaluated on the quality of their forecasts.

In this paper, we use Boolean functions or low arity and low-order polynomials as examples. We present xRAI an approach for extracting symbolic However xRAI can be applied to any function family representations of the mathematical functions efficiently learnable by a neural network. For the case of loworder a neural network was supposed to learn from the polynomials, this has been shown by [Andoni et al., trained network. The approach is based on the idea 2014]. of training a so-called interpretation network that For each family of functions, we train a neural network receives the weights and biases of the trained network called interpretation network (I-Net). The I-Net receives the as input and outputs the numerical representation weights and biases of a λ-Net as input and determines an of the function the network was supposed to approximation of a target function of the trained λ-Net. We learn that can be directly translated into a symbolic train the I-Net offline by systematically training λ-Nets on representation. We show that interpretation nets for different functions from the family and using these trained different classes of functions can be trained on synthetic networks as training examples for the I-Net.

Dependence is an important concept for many tasks in artificial intelligence. A task can be executed more efficiently by discarding something independent from the task. In this paper, we propose two novel notions of dependence in propositional logic: formula-formula dependence and formula forgetting. The first is a relation between formulas capturing whether a formula depends on another one, while the second is an operation that returns the strongest consequence independent of a formula. We also apply these two notions in two well-known issues: belief update and conservative extension. Firstly, we define a new update operator based on formula-formula dependence. Furthermore, we reduce conservative extension to formula forgetting.

Dependence is an important concept for many tasks in artificial intelligence. A task can be executed more efficiently by discarding something independent from the task. In this paper, we propose two novel notions of dependence in propositional logic: formula-formula dependence and formula forgetting. The first is a relation between formulas capturing whether a formula depends on another one, while the second is an operation that returns the strongest consequence independent of a formula. We also apply these two notions in two well-known issues: belief update and conservative extension. Firstly, we define a new update operator based on formula-formula dependence. Furthermore, we reduce conservative extension to formula forgetting.

We observed that Conjunctive Normal Form (CNF) encodings of structured SAT instances often have a set of consecutive clauses defined over a small number of Boolean variables. To exploit the pattern, we propose a transformation of CNF to an alternative representation, Conjunctive Minterm Canonical Form (CMCF). The transformation is a two-step process: CNF clauses are first partitioned into disjoint subsets such that each subset contains CNF clauses with shared Boolean variables. CNF clauses in each subset are then replaced by Minterm Canonical Form (i.e., partial solutions), which is found by enumeration. We show empirically that a simple Stochastic Local Search (SLS) solver based on CMCF can consistently achieve a higher success rate using fewer evaluations than the SLS solver WalkSAT on two representative classes of structured SAT problems.

Functional decomposition of logic circuits has profound influence on all quality aspects of the cost-effective implementation of modern digital systems. In this paper, a relational approach to the decomposition of logic circuits is proposed. This approach is parallel to the normalization of relational databases, they are governed by the same concepts of functional dependency (FD) and multi-valued dependency (MVD). It is manifest that the functional decomposition of switching function actually exploits the same idea and serves a similar purpose as database normalization. Partitions play an important role in the decomposition. The interdependency of two partitions can be represented by a bipartite graph. We demonstrate that both FD and MVD can be represented by bipartite graphs with specific topological properties, which are delineated by partitions of minterms. It follows that our algorithms are procedures of constructing those specific bipartite graphs of interest to meet the information-lossless criteria of functional decomposition.