Repairing Inconsistent Databases: A Model-Theoretic Approach and Abductive Reasoning

In this paper we consider two points of views to the problem of coherent integration of distributed data. First we give a pure model-theoretic analysis of the possible ways to `repair' a database. We do so by characterizing the possibilities to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. Then we introduce an abductive application to restore the consistency of a given database. This application is based on an abductive solver (A-system) that implements an SLDNFA-resolution procedure, and computes a list of data-facts that should be inserted to the database or retracted from it in order to keep the database consistent. The two approaches for coherent data integration are related by soundness and completeness results.Comment: 15 pages. Originally published in proc. PCL 2002, a FLoC workshop; eds. Hendrik Decker, Dina Goldin, Jorgen Villadsen, Toshiharu Waragai (http://floc02.diku.dk/PCL/

A New Algorithm to Automate Inductive Learning of Default Theories

In inductive learning of a broad concept, an algorithm should be able to distinguish concept examples from exceptions and noisy data. An approach through recursively finding patterns in exceptions turns out to correspond to the problem of learning default theories. Default logic is what humans employ in common-sense reasoning. Therefore, learned default theories are better understood by humans. In this paper, we present new algorithms to learn default theories in the form of non-monotonic logic programs. Experiments reported in this paper show that our algorithms are a significant improvement over traditional approaches based on inductive logic programming.Comment: Paper presented at the 33rd International Conference on Logic Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017 16 pages, LaTeX, 3 PDF figures (arXiv:YYMM.NNNNN

Location-Based Reasoning about Complex Multi-Agent Behavior

Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical-relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as a player capturing an enemy. Our unified model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. Further, we show that given a model of successfully performed multi-agent activities, along with a set of examples of failed attempts at the same activities, our system automatically learns an augmented model that is capable of recognizing success and failure, as well as goals of peoples actions with high accuracy. We compare our approach with other alternatives and show that our unified model, which takes into account not only relationships among individual players, but also relationships among activities over the entire length of a game, although more computationally costly, is significantly more accurate. Finally, we demonstrate that explicitly modeling unsuccessful attempts boosts performance on other important recognition tasks

All-Path Reachability Logic

This paper presents a language-independent proof system for reachability properties of programs written in non-deterministic (e.g., concurrent) languages, referred to as all-path reachability logic. It derives partial-correctness properties with all-path semantics (a state satisfying a given precondition reaches states satisfying a given postcondition on all terminating execution paths). The proof system takes as axioms any unconditional operational semantics, and is sound (partially correct) and (relatively) complete, independent of the object language. The soundness has also been mechanized in Coq. This approach is implemented in a tool for semantics-based verification as part of the K framework (http://kframework.org

Unified Correspondence and Proof Theory for Strict Implication

The unified correspondence theory for distributive lattice expansion logics (DLE-logics) is specialized to strict implication logics. As a consequence of a general semantic consevativity result, a wide range of strict implication logics can be conservatively extended to Lambek Calculi over the bounded distributive full non-associative Lambek calculus (BDFNL). Many strict implication sequents can be transformed into analytic rules employing one of the main tools of unified correspondence theory, namely (a suitably modified version of) the Ackermann lemma based algorithm \msf{ALBA}. Gentzen-style cut-free sequent calculi for BDFNL and its extensions with analytic rules which are transformed from strict implication sequents, are developed.Comment: This is a Pre-publication version of a submission to the Journal of Logic and Computatio

MirrorShard: Proof by Computational Reflection with Verified Hints

We describe a method for building composable and extensible verification procedures within the Coq proof assistant. Unlike traditional methods that rely on run-time generation and checking of proofs, we use verified-correct procedures with Coq soundness proofs. Though they are internalized in Coq's logic, our provers support sound extension by users with hints over new domains, enabling automated reasoning about user-defined abstract predicates. We maintain soundness by developing an architecture for modular packaging, construction, and composition of hint databases, which had previously only been implemented in Coq at the level of its dynamically typed, proof-generating tactic language. Our provers also include rich handling of unification variables, enabling integration with other tactic-based deduction steps within Coq. We have implemented our techniques in MirrorShard, an open-source framework for reflective verification. We demonstrate its applicability by instantiating it to separation logic in order to reason about imperative program verification

Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs

The process of identifying changes or transformations in a scene along with the ability of reasoning about their causes and effects, is a key aspect of intelligence. In this work we go beyond recent advances in computational perception, and introduce a more challenging task, Image-based Event-Sequencing (IES). In IES, the task is to predict a sequence of actions required to rearrange objects from the configuration in an input source image to the one in the target image. IES also requires systems to possess inductive generalizability. Motivated from evidence in cognitive development, we compile the first IES dataset, the Blocksworld Image Reasoning Dataset (BIRD) which contains images of wooden blocks in different configurations, and the sequence of moves to rearrange one configuration to the other. We first explore the use of existing deep learning architectures and show that these end-to-end methods under-perform in inferring temporal event-sequences and fail at inductive generalization. We then propose a modular two-step approach: Visual Perception followed by Event-Sequencing, and demonstrate improved performance by combining learning and reasoning. Finally, by showing an extension of our approach on natural images, we seek to pave the way for future research on event sequencing for real world scenes.Comment: 10 pages, 5 figures, for associated dataset, see https://asu-active-perception-group.github.io/bird_dataset_web

The Next 700 Challenge Problems for Reasoning with Higher-Order Abstract Syntax Representations: Part 1-A Common Infrastructure for Benchmarks

A variety of logical frameworks support the use of higher-order abstract syntax (HOAS) in representing formal systems. Although these systems seem superficially the same, they differ in a variety of ways; for example, how they handle a context of assumptions and which theorems about a given formal system can be concisely expressed and proved. Our contributions in this paper are three-fold: 1) we develop a common infrastructure for representing benchmarks for systems supporting reasoning with binders, 2) we present several concrete benchmarks, which highlight a variety of different aspects of reasoning within a context of assumptions, and 3) we design an open repository ORBI, (Open challenge problem Repository for systems supporting reasoning with BInders). Our work sets the stage for providing a basis for qualitative comparison of different systems. This allows us to review and survey the state of the art, which we do in great detail for four systems in Part 2 of this paper (Felty et al, 2015). It also allows us to outline future fundamental research questions regarding the design and implementation of meta-reasoning systems.Comment: 42 pages, 5 figure

Computing Stable Models of Normal Logic Programs Without Grounding

We present a method for computing stable models of normal logic programs, i.e., logic programs extended with negation, in the presence of predicates with arbitrary terms. Such programs need not have a finite grounding, so traditional methods do not apply. Our method relies on the use of a non-Herbrand universe, as well as coinduction, constructive negation and a number of other novel techniques. Using our method, a normal logic program with predicates can be executed directly under the stable model semantics without requiring it to be grounded either before or during execution and without requiring that its variables range over a finite domain. As a result, our method is quite general and supports the use of terms as arguments, including lists and complex data structures. A prototype implementation and non-trivial applications have been developed to demonstrate the feasibility of our method

Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining

We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and utilities respectively. We make use of TreeExplainer, a fast and scalable implementation of the Explainable AI tool SHAP, to extract locally important features and their weights from ensemble tree models. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and running time of the training algorithm compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.Comment: arXiv admin note: text overlap with arXiv:1808.0062