A new approach to hybrid probabilistic logic programs

This paper presents a novel revision of the framework of Hybrid Probabilistic Logic Programming, along with a complete semantics characterization, to enable the encoding of and reasoning about real-world applications. The language of Hybrid Probabilistic Logic Programs framework is extended to allow the use of non-monotonic negation, and two alternative semantical characterizations are defined: stable probabilistic model semantics and probabilistic well-founded semantics. These semantics generalize the stable model semantics and well-founded semantics of traditional normal logic programs, and they reduce to the semantics of Hybrid Probabilistic Logic programs for programs without negation. It is the first time that two different semantics for Hybrid Probabilistic Programs with non-monotonic negation as well as their relationships are described. This proposal provides the foundational grounds for developing computational methods for implementing the proposed semantics. Furthermore, it makes it clearer how to characterize non-monotonic negation in probabilistic logic programming frameworks for commonsense reasoning. An erratum to this article can be found at     

Programs as visual,interactive documents

We present a novel approach to combined textual and visual programming by allowing visual, interactive objects to be embedded within textual source code and segments of source code to be further embedded within those objects. We retain the strengths of text‐based source code, while enabling visual programming where it is beneficial. Additionally, embedded objects and code provide a simple object‐oriented approach to adding a visual form of LISP‐style macros to a language. The ability to freely combine source code and visual, interactive objects with one another allows for the construction of interactive programming tools and experimentation with novel programming language extensions. Our visual programming system is supported by a type coercion‐based presentation protocol that displays normal Java and Python objects in a visual, interactive form. We have implemented our system within a prototype interactive programming environment called ‘The Larch Environment’. Copyright © 2013 John Wiley & Sons, Ltd.     

Exploring the Effect of Control-Flow and Traversal Direction on VPL Usability for Novices

Programming is a hard cognitive activity, especially for novices who also have to struggle with learning the intricacies of the programming language syntax. We postulate that a well-designed diagrammatic visual programming language (VPL) to replace or substitute a textual programming language may help in the learning programming process. Our research focuses on the suitability of diagrammatic notations for such a VPL. In this paper, we report results from two experimental studies into diagrammatic notations. The first experiment shows the superiority of three visual representations over a conventional style textual program in both control- and data-flow paradigms. The result also shows the effect of programming paradigm on novices' performance. The second experiment focuses on two aspects of program flow representation: the graphical representation to be used for sequencing, and the traversal direction required of diagram users. It reveals that graphical representation does not affect performance in tracing program flow but that traversal direction makes a difference in cognitive demand on users. The evidence also indicates a control-flow preference among novices.     

Probabilistic logic programming

Of all scientific investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming have restricted themselves to non-probabilistic semantic characterizations. In this paper, we take a few steps towards rectifying this situation. We define a logic programming language that is syntactically similar to the annotated logics of Blair et al., 1987 and Blair and Subrahmanian, 1988, 45–73) but in which the truth values are interpreted probabilistically. A probabilistic model theory and fixpoint theory is developed for such programs. This probabilistic model theory satisfies the requirements proposed by Fenstad (in “Studies in Inductive Logic and Probabilities” (R. C. Jeffrey, Ed.), Vol. 2, pp. 251–262, Univ. of California Press, Berkeley, 1980) for a function to be called probabilistic. The logical treatment of probabilities is complicated by two facts: first, that the connectives cannot be interpreted truth-functionally when truth values are regarded as probabilities; second, that negation-free definite-clause-like sentences can be inconsistent when interpreted probabilistically. We address these issues here and propose a formalism for probabilistic reasoning in logic programming. To our knowledge, this is the first probabilistic characterization of logic programming semantics.     

Explaining the impact of source behaviour in evidential reasoning

Explanation abilities are required for data-driven models, where the high number of parameters may render its internal reasoning opaque to users. Despite the natural transparency brought by the graphical model structure of Bayesian networks, decisions trees or valuation networks, additional explanation abilities are still required due to both the complexity of the problem as well as the consequences of the decision to be taken. Threat assessment is an example of such a complex problem in which several sources with partially unknown behaviour provide information on distinct but related frames of discernment. In this paper, we propose a solution as an evidential network with explanation abilities to detect and investigate threat to maritime infrastructure. We propose a post-hoc explanation approach to an already transparent by design threat assessment model, combining feature relevance and natural language explanations with some visual support. To this end, we extend the sensitivity analysis method of generation of explanations for evidential reasoning to a multi-source model where sources can have several and disparate behaviours. Natural language explanations are generated on the basis of a series of sensitivity measures quantifying the impact of both direct reports and source models. We conclude on challenges to be addressed in future work.     

An Evaluation of Explanations of Probabilistic Inference

Providing explanations of the conclusions of decision-support systems can be viewed as presenting inference results in a manner that enhances the user's insight into how these results were obtained. The ability to explain inferences has been demonstrated to be an important factor in making medical decision-support systems acceptable for clinical use. Although many researchers in artificial intelligence have explored the automatic generation of explanations for decision-support systems based on symbolic reasoning, research in automated explanation of probabilistic results has been limited. We present the results of an evaluation study of INSITE, a program that explains the reasoning of decision-support systems based on Bayesian belief networks. In the domain of anesthesia, we compared subjects who had access to a belief network with explanations of the inference results to control subjects who used the same belief network without explanations. We show that, compared to control subjects, the explanation subjects demonstrated greater diagnostic accuracy, were more confident about their conclusions, were more critical of the belief network, and found the presentation of the inference results more clear.     

Action to process: constructing functions from algebra word problems

Abstract

The ability to build a mathematical model of a situation described in ordinary language has always been an important goal of school mathematics teaching. This paper gives one explanation for the difficulties many students experience with certain stylized modeling tasks (solving algebra word problems) and puts these difficulties in the context of a theory about the acquisition of the concept of mathematical function. It also describes a visual programming language that can act as a bridge, and in some sense an alternative, to formal algebraic notation as a language for describing situations. Tracing the author's classroom experiments and his reflections on those experiments, the paper suggests a method for helping students.     

Monaco—A domain-specific language solution for reactive process control programming with hierarchical components

In this paper, we present Monaco – a domain-specific language for developing event-based, reactive process control programs – and its visual interactive programming environment. The main purpose of the language is to bring process control programming closer to domain experts. Important design goals have therefore been to keep the language concise and to allow programs to be written that reflect the perceptions of domain experts. Monaco is similar to Statecharts in its expressive power, but adopts an imperative notation. Moreover, Monaco uses a state-of-the-art component approach with interfaces and polymorphic implementations, and enforces strict hierarchical component architectures that support hierarchical abstraction of control functionality. We present the main design goals, the essential programming elements, the visual interactive programming environment, results from industrial case studies, and a formal definition of the semantics of the reactive behavior of Monaco programs in the form of labeled transition systems.     

The prospects for an evolutionary psychology: Human language and human reasoning

Evolutionary psychology purports to explain human capacities as adaptations to an ancestral environment. A complete explanation of human language or human reasoning as adaptations depends on assessing an historical claim, that these capacities evolved under the pressure of natural selection and are prevalent because they provided systematic advantages to our ancestors. An outline of the character of the information needed in order to offer complete adaptation explanations is drawn from Robert Brandon (1990), and explanations offered for the evolution of language and reasoning within evolutionary psychology are evaluated. Pinker and Bloom's (1992) defense of human language as an adaptation for verbal communication, Robert Nozick's (1993) account of the evolutionary origin of rationality, and Cosmides and Tooby's (1992) explanation of human reasoning as an adaptation for social exchange, are discussed in light of what is known, and what is not known, about the history of human evolution. In each case, though a plausible case is made that these capacities are adaptations, there is not enough known to offer even a semblance of an explanation of the origin of these capacities. These explanations of the origin of human thought and language are simply speculations lacking the kind of detailed historical information required for an evolutionary explanation of an adaptation.     

Computing with graphs and graph transformations

Many software applications require the construction and manipulation of graphs. In standard programming languages, this is accomplished using low‐level mechanisms such as pointer manipulation or array indexing. In contrast, graph productions are a convenient high‐level visual notation for coding graph modifications. A graph production replaces one subgraph by another subgraph. Graph productions can define a graph grammar and graph language, or can directly transform an input graph into an output graph. Graph transformation has been applied in many areas, including the definition of visual languages and their tools, the construction of software development environments, the definition of constraint programming algorithms, the modeling of distributed systems, and the construction of neural networks. One application is presented in detail: the interpretation of mathematical notation in scanned document images. The graph models the set of mathematical symbols, and their spatial and logical relationships. This graph is transformed by productions written in the PROGRES language. Copyright © 1999 John Wiley & Sons, Ltd.     

DMRFNet: Deep Multimodal Reasoning and Fusion for Visual Question Answering and explanation generation

Visual Question Answering (VQA), which aims to answer questions in natural language according to the content of image, has attracted extensive attention from artificial intelligence community. Multimodal reasoning and fusion is a central component in recent VQA models. However, most existing VQA models are still insufficient to reason and fuse clues from multiple modalities. Furthermore, they are lack of interpretability since they disregard the explanations. We argue that reasoning and fusing multiple relations implied in multimodalities contributes to more accurate answers and explanations. In this paper, we design an effective multimodal reasoning and fusion model to achieve fine-grained multimodal reasoning and fusion. Specifically, we propose Multi-Graph Reasoning and Fusion (MGRF) layer, which adopts pre-trained semantic relation embeddings, to reason complex spatial and semantic relations between visual objects and fuse these two kinds of relations adaptively. The MGRF layers can be further stacked in depth to form Deep Multimodal Reasoning and Fusion Network (DMRFNet) to sufficiently reason and fuse multimodal relations. Furthermore, an explanation generation module is designed to justify the predicted answer. This justification reveals the motive of the model’s decision and enhances the model’s interpretability. Quantitative and qualitative experimental results on VQA 2.0, and VQA-E datasets show DMRFNet’s effectiveness.     

An old new notation for elementary probability theory

The Eindhoven approach to quantifier notation is 40 years old. We extend it by adding “distribution comprehensions” systematically to its repertoire; we believe the resulting notation for elementary probability theory is new.After a step-by-step explanation of the proposed notational innovations, with small examples, we give as our exemplary case study the probabilistic reasoning associated with a quantitative noninterference semantics based on Hidden Markov Models of computation. Although that example was the motivation for this work, we believe the proposal here will be more generally applicable: and so we also revisit a number of popular puzzles, to illustrate the notation's wider utility.Finally, we review the connection between comprehension notations and (category-theoretic) monads, and show how the Haskell approach to monad comprehensions applies to the distribution comprehensions we have introduced.     

The analysis of architectural languages for the needs of practitioners

Architectural languages (ALs) have attracted much attention as the modeling notations for specifying and reasoning about important design decisions. In this study, 124 different existing ALs have been analyzed for a set of requirements that are crucial for practitioners. These requirements are concerned with language definition, language features, and tool support. Some of the important findings obtained from the analysis are as follows: (1) performance is the top popular nonfunctional requirement supported by ALs; (2) no ALs offer both textual and visual notation sets, one of which could be used independently; (3) process algebras are the top preferred formal method by formal ALs; (4) the physical, deployment, and operational viewpoints are rarely supported by ALs; (5) the top preferred extension mechanism of the extensible ALs is XML for syntax extension; (6) Java is the top preferred programming language in generating software code; (7) the exhaustive model checking is the top preferred automated analysis method; (8) the logic‐based formal techniques are so popular in specifying system requirements; (9) among the analysis properties considered, consistency is the top supported property for the automated checking; and (10) most ALs do not provide any discussion platform (eg, forums). Hence, these findings can be used by the new AL developers in addressing the needs of practitioners and bridging the gaps in the field. Practitioners can also use the findings to find out about the existing ALs and compare them to choose the one(s) that suits their needs best.     

PRL: A probabilistic relational language

In this paper, we describe the syntax and semantics for a probabilistic relational language (PRL). PRL is a recasting of recent work in Probabilistic Relational Models (PRMs) into a logic programming framework. We show how to represent varying degrees of complexity in the semantics including attribute uncertainty, structural uncertainty and identity uncertainty. Our approach is similar in spirit to the work in Bayesian Logic Programs (BLPs), and Logical Bayesian Networks (LBNs). However, surprisingly, there are still some important differences in the resulting formalism; for example, we introduce a general notion of aggregates based on the PRM approaches. One of our contributions is that we show how to support richer forms of structural uncertainty in a probabilistic logical language than have been previously described. Our goal in this work is to present a unifying framework that supports all of the types of relational uncertainty yet is based on logic programming formalisms. We also believe that it facilitates understanding the relationship between the frame-based approaches and alternate logic programming approaches, and allows greater transfer of ideas between them. Editors: Hendrik Blockeel, David Jensen and Stefan Kramer An erratum to this article is available at .     

COCOVILA – Compiler-Compiler for Visual Languages

A compiler-compiler for visual languages is presented. It has been designed as a framework for building visual programming environments that translate schemas into textual representation as well as into programs representing the deep meaning of schemas. The deep semantics is implemented by applying attribute grammars to schema languages; attribute dependencies are implemented as methods of Java classes. Unlike compiler-compilers of textual languages, a large part of the framework is needed for support of interactive usage of a visual language.     

A Graph-Rewriting Visual Language for Database Programming

Textual database programming languages are computationally complete, but have the disadvantage of giving the user a non-intuitive view of the database information that is being manipulated. The visual languages developed in recent years have allowed naive users access to a direct representation of data, often in a graph form, but have concentrated on user interface rather than complex programming tasks. There is a need for a system which combines the advantages of both these programming methods.We describe an implementation of Spider, an experimental visual database programming language aimed at programmers. It uses a graph-rewriting paradigm as a basis for a fully visual, computationally complete language. The graphs it rewrites represent the schema and instances of a database.The unique graph-rewriting method used by Spider has syntactic and semantic simplicity. Its form of algorithmic expression allows complex computation to be easily represented in short programs. Furthermore, Spider has greater power than normally provided in textual systems, and we show that queries on the schema and associative queries can be performed easily and without requiring any additions to the language.     

Design, construction, and application of a generic visual languagegeneration environment

The implementation of visual programming languages (VPLs) and their supporting environments is time-consuming and tedious. To ease the task, researchers have developed some high-level tools to reduce the development effort. None of these tools, however, can be easily used to create a complete visual language in a seamless way as the lex/yacc tools do for textual language constructions. This paper presents the design, construction and application of a generic visual language generation environment, called VisPro. The VisPro design model improves the conventional model-view-controller framework in that its functional modules are decoupled to allow independent development and integration. The VisPro environment consists of a set of visual programming tools. Using VisPro, the process of VPL construction can be divided into two steps: lexicon definition and grammar specification. The former step defines visual objects and a visual editor, and the latter step provides language grammars with graph rewriting rules. The compiler for the VPL is automatically created according to the grammar specification. A target VPL is generated as a programming environment which contains the compiler and the visual editor. The paper demonstrates how VisPro is used by building a simple visual language and a more complex visual modeling language for distributed programming