Comparative study of connectionist simulators

This paper presents practical experiences and results we obtained while working with simulators for artificial neural network, i.e. a comparison of the simulators\u27 functionality and performance is described. The selected simulators are free of charge for research and education. The simulators in test were: (a) PlaNet, Version 5.6 from the University of Colorado at Boulder, USA, (b) Pygmalion, Version 2.0, from the Computer Science Department of the University College London, Great Britain, (c) the Rochester Connectionist Simulator (RCS), Version 4.2 from the University of Rochester, NY, USA and (d) the SNNS (Stuttgart Neural Net Simulator), Versions 1.3 and 2.0 from the University of Stuttgart, Germany. The functionality test focusses on special features concerning the establishment and training of connectionist networks as well as facilities of their application. By exemplarily evaluating the simulators\u27 performance, we attempted to establish one and the same type of back-propagation network for optical character recognition (OCR). A respective quality statement is made by comparing the number of cycles needed for training and the recognition rate of the individual simulators

Designing a structured lexicon for document image analysis

This paper presents a structured, multi-level architecture of a lexicon which is a central component of our knowledge-based document analysis system. Our system has the task to transform incoming business letters into an equivalent electronic representation automatically. Moreover, partial text analysis and understanding of a letter\u27s body and relevant parts are initiated to enrich the conceptual knowledge about the actual document (e.g., by a classification). In such an application domain, a well-designed lexicon has to consider requirements of both, text recognition and text analysis. For that purpose, we propose an appropriate lexicon architecture and the internal structure of corresponding lexical entries being a prerequisite for successful higher-level interpretations of documents

Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach

We are interested in image manipulation via natural language text -- a task that is useful for multiple AI applications but requires complex reasoning over multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning (NSCL), which has been quite effective for the task of Visual Question Answering (VQA), for the task of image manipulation. Our system referred to as NeuroSIM can perform complex multi-hop reasoning over multi-object scenes and only requires weak supervision in the form of annotated data for VQA. NeuroSIM parses an instruction into a symbolic program, based on a Domain Specific Language (DSL) comprising of object attributes and manipulation operations, that guides its execution. We create a new dataset for the task, and extensive experiments demonstrate that NeuroSIM is highly competitive with or beats SOTA baselines that make use of supervised data for manipulation.Comment: EMNLP 2023 (long paper, main conference

Integration of document representation, processing and management

This paper describes a way for document representation and proposes an approach towards an integrated document processing and management system. The approach has the intention to capture essentially freely structured documents, like those typically used in the office domain. The document analysis system ANASTASIL is capable to reveal the structure of complex paper documents, as well as logical objects within it, like receiver, footnote, date. Moreover, it facilitates the handling of the containing information. Analyzed documents are stored by the management system KRISYS that is connected to several different subsequent services. The described integrated system can be considered as an ideal extension of the human clerk, making his tasks in information processing easier. The symbolic representation of the analysis results allow an easy transformation in a given international standard, e.g., ODA/ODIF or SGML, and to interchange it via global network

Categorical invariance and structural complexity in human concept learning

An alternative account of human concept learning based on an invariance measure of the categorical\ud stimulus is proposed. The categorical invariance model (CIM) characterizes the degree of structural\ud complexity of a Boolean category as a function of its inherent degree of invariance and its cardinality or\ud size. To do this we introduce a mathematical framework based on the notion of a Boolean differential\ud operator on Boolean categories that generates the degrees of invariance (i.e., logical manifold) of the\ud category in respect to its dimensions. Using this framework, we propose that the structural complexity\ud of a Boolean category is indirectly proportional to its degree of categorical invariance and directly\ud proportional to its cardinality or size. Consequently, complexity and invariance notions are formally\ud unified to account for concept learning difficulty. Beyond developing the above unifying mathematical\ud framework, the CIM is significant in that: (1) it precisely predicts the key learning difficulty ordering of\ud the SHJ [Shepard, R. N., Hovland, C. L.,&Jenkins, H. M. (1961). Learning and memorization of classifications.\ud Psychological Monographs: General and Applied, 75(13), 1-42] Boolean category types consisting of three\ud binary dimensions and four positive examples; (2) it is, in general, a good quantitative predictor of the\ud degree of learning difficulty of a large class of categories (in particular, the 41 category types studied\ud by Feldman [Feldman, J. (2000). Minimization of Boolean complexity in human concept learning. Nature,\ud 407, 630-633]); (3) it is, in general, a good quantitative predictor of parity effects for this large class of\ud categories; (4) it does all of the above without free parameters; and (5) it is cognitively plausible (e.g.,\ud cognitively tractable)

The first-order theory of lexicographic path orderings is undecidable

We show, under some assumption on the signature, that the *This formula not viewable on a Text-Browser* fragment of the theory of any lexicographic path ordering is undecidable. This applies to partial and to total precedences. Our result implies in particular that the simplification rule of ordered completion is undecidable

On abduction and answer generation through constrained resolution

Recently, extensions of constrained logic programming and constrained resolution for theorem proving have been introduced, that consider constraints, which are interpreted under an open world assumption. We discuss relationships between applications of these approaches for query answering in knowledge base systems on the one hand and abduction-based hypothetical reasoning on the other hand. We show both that constrained resolution can be used as an operationalization of (some limited form of) abduction and that abduction is the logical status of an answer generation process through constrained resolution, ie., it is an abductive but not a deductive form of reasoning