Recent work in natural language processing has looked at learning text to code translation models using parallel pairs of text and code samples from example source code libraries (for a review, see Neubig (2016)). In particular, Richardson and Kuhn (2017a,b); Richardson et al. (2018) look at learning to translate short text descriptions to function signature representations as a first step towards modeling the semantics of function documentation. Examples pairs of docstring and function signature representations are shown in Figure 1; using such pairs, the goal is to learn a general model that can robustly translate a given description of a function to a formal representation of that function. Initially, these datasets were proposed as a synthetic resource for studying semantic parser induction (Mooney, 2007), or for building models that learn to translate text to formal meaning representations from parallel data (see Richardson et al. (2017) for a proposal on using these datasets for the inverse problem of data-to-text generation). To date, we have built around 45 API datasets across 11 popular programming languages (e.g., Python, Java, C, Scheme, Haskell, PHP) and 7 natural languages (see Richardson (2017)), each using an ad hoc rendering of the target function signature representations. In this brief note, we define a unified syntax for expressing these representations, as well as a systematic mapping into first-order logic and a small subject domain model. In doing this, we aim to answer the following question: what do these function signatures that are being learned actually mean, and how can they be used for solving more complex natural language understanding problems (for a similar idea, see Bos (2016))? By recasting the learned representations in terms of classical logic, the hope is that our datasets will in particular be made more accessible to studies on natural language based program synthesis (Raza et al., 2015) and natural language programming more generally. In what follows, we first define a general syntax for these representations, then discuss the mapping into logic and the various applications that motivate our particular approach and subject domain model.

In one of the most common settings in statistical relational learning (SRL), we are given a single relational structure from which we need to learn a model, and this model is then used to make predictions on previously unseen structures. For example, the global relational structure could correspond to a large social network, with the training data specifying the relationships that hold among a small subset of the users, along with their attributes. Clearly, in order to provide any guarantees on the accuracy of these predictions, we need to make (simplifying) assumptions about how the training and test structures are related. In this paper, we follow the setting from [10, 9], where it is assumed that these structures, which we will call relational examples, are all obtained by sampling domain elements from a larger global structure (uniformly and without replacement).

More than 50 years ago Bongard introduced 100 visual concept learning problems as a testbed for intelligent vision systems. These problems are now known as Bongard problems. Although they are well known in the cognitive science and AI communities only moderate progress has been made towards building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing complex visual concepts based on this vocabulary. Using this language and Bayesian inference, complex visual concepts can be induced from the examples that are provided in each Bongard problem. Contrary to other concept learning problems the examples from which concepts are induced are not random in Bongard problems, instead they are carefully chosen to communicate the concept, hence requiring pragmatic reasoning. Taking pragmatic reasoning into account we find good agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself. While this approach is far from solving all Bongard problems, it solves the biggest fraction yet.

Game generation and analysis has commonly relied on hand authored rules and heuristics.  This authoring task comes with a high authorial burden, both in the amount of rules and heuristics that need to be authored for decent coverage and in the complexity of authoring these rules.  In this paper I present early work on \textit{Leda} and inductive logic programming system designed to learn these rules, so as to support further generation and analysis.  I present Leda, describe its process, and finally show a sample set of the rules that it learns.

In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.

This paper describes the architecture that integrates DALI MASs (Multi-Agent Systems) and ASP (Answer Set Programming) modules for reaching goals in a flexible and timely way, where DALI is a computational-logic-based fully implemented agent-oriented logic programming language and ASP modules includes solvers that allow affordable and flexible planning capabilities. The proposed DALI MAS architecture exploits such modules for flexible goal decomposition and planning, with the possibility to select plans according to a suite of possible preferences and to re-plan upon need. We present an abstract case-study concerning DALI agents which cooperate for exploring an unknown territory under changing circumstances in an optimal or at least sub-optimal fashion. The architecture can be exploited not only by DALI agents, but rather by any kind of logical agent.

We contribute to recent efforts in relating two approaches to automatic synthesis, namely, automated planning and discrete reactive synthesis. First, we develop a declarative characterization of the standard "fairness" assumption on environments in non-deterministic planning, and show that strong-cyclic plans are correct solution concepts for fair environments. This complements, and arguably completes, the existing foundational work on non-deterministic planning, which focuses on characterizing (and computing) plans enjoying special "structural" properties, namely loopy but closed policy structures. Second, we provide an encoding suitable for reactive synthesis that avoids the naive exponential state space blowup. To do so, special care has to be taken to specify the fairness assumption on the environment in a succinct manner.

This is one of the few texts that combines three essential theses in the study of logic programming: the logic that gives logic programs their unique character: the practice of programming effectively using the logic; and the efficient implementation of logic programming on computers.

We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden. Moreover, up to two edges are allowed between any pair of nodes. Specifically, we present local, pairwise and global Markov properties for the new graphical models and prove their equivalence. We also present an equivalent factorization property. Finally, we present a causal interpretation of the new models.

Over these years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn Answer Set Programs. We present a sound and complete algorithm which takes the input in a slightly different manner and perform an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available at https://goo.gl/KdWAcV.