Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

Radical Artificial Intelligence: A Postmodern Approach

The dynamic response of end-clamped monolithic beams and sandwich beams has been measured by loading the beams at mid-span using metal foam projectiles. The AISI 304 stainless-steel sandwich beams comprise two identical face sheets and either prismatic Y-frame or corrugated cores. The resistance to shock loading is quantified by the permanent transverse deflection at mid-span of the beams as a function of projectile momentum. The prismatic cores are aligned either longitudinally along the beam length or transversely. It is found that the sandwich beams with a longitudinal core orientation have a higher shock resistance than the monolithic beams of equal mass. In contrast, the performance of the sandwich beams with a transverse core orientation is very similar to that of the monolithic beams. Three-dimensional finite element (FE) simulations are in good agreement with the measured responses. The FE calculations indicate that strain concentrations in the sandwich beams occur at joints within the cores and between the core and face sheets; the level of maximum strain is similar for the Y-frame and corrugated core beams for a given value of projectile momentum. The experimental and FE results taken together reveal that Y-frame and corrugated core sandwich beams of equal mass have similar dynamic performances in terms of rear-face deflection, degree of core compression and level of strain within the beam

A connectionist representation of first-order formulae with dynamic variable binding

The relationship between symbolicism and connectionism has been one of the major issues in recent Artificial Intelligence research. An increasing number of researchers from each side have tried to adopt desirable characteristics of the other. These efforts have produced a number of different strategies for interfacing connectionist and sym¬ bolic AI. One of them is connectionist symbol processing which attempts to replicate symbol processing functionalities using connectionist components.In this direction, this thesis develops a connectionist inference architecture which per¬ forms standard symbolic inference on a subclass of first-order predicate calculus. Our primary interest is in understanding how formulas which are described in a limited form of first-order predicate calculus may be implemented using a connectionist archi¬ tecture. Our chosen knowledge representation scheme is a subset of first-order Horn clause expressions which is a set of universally quantified expressions in first-order predicate calculus. As a focus of attention we are developing techniques for compiling first-order Horn clause expressions into a connectionist network. This offers practical benefits but also forces limitations on the scope of the compiled system, since we tire, in fact, merging an interpreter into the connectionist networks. The compilation process has to take into account not only first-order Horn clause expressions themselves but also the strategy which we intend to use for drawing inferences from them. Thus, this thesis explores the extent to which this type of a translation can build a connectionist inference model to accommodate desired symbolic inference.This work first involves constructing efficient connectionist mechanisms to represent basic symbol components, dynamic bindings, basic symbolic inference procedures, and devising a set of algorithms which automatically translates input descriptions to neural networks using the above connectionist mechanisms. These connectionist mechanisms are built by taking an existing temporal synchrony mechanism and extending it further to obtain desirable features to represent and manipulate basic symbol structures. The existing synchrony mechanism represents dynamic bindings very efficiently using tem¬ poral synchronous activity between neuron elements but it has fundamental limitations in supporting standard symbolic inference. The extension addresses these limitations.The ability of the connectionist inference model was tested using various types of first order Horn clause expressions. The results showed that the proposed connectionist in¬ ference model was able to encode significant sets of first order Horn clause expressions and replicated basic symbolic styles of inference in a connectionist manner. The system successfully demonstrated not only forward chaining but also backward chaining over the networks encoding the input expressions. The results, however, also showed that implementing a connectionist mechanism for full unification among groups of unifying arguments in rules, are encoding some types of rules, is difficult to achieve in a con¬ nectionist manner needs additional mechanisms. In addition, some difficult issues such as encoding rules having recursive definitions remained untouched

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book

Encoding techniques for complex information structures in connectionist systems

Two general information encoding techniques called relative position encoding and pattern similarity association are presented. They are claimed to be a convenient basis for the connectionist implementation of complex, short term information processing of the sort needed in common sense reasoning, semantic/pragmatic interpretation of natural language utterances, and other types of high level cognitive processing. The relationships of the techniques to other connectionist information-structuring methods, and also to methods used in computers, are discussed in detail. The rich inter-relationships of these other connectionist and computer methods are also clarified. The particular, simple forms are discussed that the relative position encoding and pattern similarity association techniques take in the author's own connectionist system, called Conposit, in order to clarify some issues and to provide evidence that the techniques are indeed useful in practice

Domain-Specific Knowledge Acquisition for Conceptual Sentence Analysis

The availability of on-line corpora is rapidly changing the field of natural language processing (NLP) from one dominated by theoretical models of often very specific linguistic phenomena to one guided by computational models that simultaneously account for a wide variety of phenomena that occur in real-world text. Thus far, among the best-performing and most robust systems for reading and summarizing large amounts of real-world text are knowledge-based natural language systems. These systems rely heavily on domain-specific, handcrafted knowledge to handle the myriad syntactic, semantic, and pragmatic ambiguities that pervade virtually all aspects of sentence analysis. Not surprisingly, however, generating this knowledge for new domains is time-consuming, difficult, and error-prone, and requires the expertise of computational linguists familiar with the underlying NLP system. This thesis presents Kenmore, a general framework for domain-specific knowledge acquisition for conceptual sentence analysis. To ease the acquisition of knowledge in new domains, Kenmore exploits an on-line corpus using symbolic machine learning techniques and robust sentence analysis while requiring only minimal human intervention. Unlike most approaches to knowledge acquisition for natural language systems, the framework uniformly addresses a range of subproblems in sentence analysis, each of which traditionally had required a separate computational mechanism. The thesis presents the results of using Kenmore with corpora from two real-world domains (1) to perform part-of-speech tagging, semantic feature tagging, and concept tagging of all open-class words in the corpus; (2) to acquire heuristics for part-ofspeech disambiguation, semantic feature disambiguation, and concept activation; and (3) to find the antecedents of relative pronouns

On the application of neural networks to symbol systems.

While for many years two alternative approaches to building intelligent systems, symbolic AI and neural networks, have each demonstrated specific advantages and also revealed specific weaknesses, in recent years a number of researchers have sought methods of combining the two into a unified methodology which embodies the benefits of each while attenuating the disadvantages. This work sets out to identify the key ideas from each discipline and combine them into an architecture which would be practically scalable for very large network applications. The architecture is based on a relational database structure and forms the environment for an investigation into the necessary properties of a symbol encoding which will permit the singlepresentation learning of patterns and associations, the development of categories and features leading to robust generalisation and the seamless integration of a range of memory persistencies from short to long term. It is argued that if, as proposed by many proponents of symbolic AI, the symbol encoding must be causally related to its syntactic meaning, then it must also be mutable as the network learns and grows, adapting to the growing complexity of the relationships in which it is instantiated. Furthermore, it is argued that in order to create an efficient and coherent memory structure, the symbolic encoding itself must have an underlying structure which is not accessible symbolically; this structure would provide the framework permitting structurally sensitive processes to act upon symbols without explicit reference to their content. Such a structure must dictate how new symbols are created during normal operation. The network implementation proposed is based on K-from-N codes, which are shown to possess a number of desirable qualities and are well matched to the requirements of the symbol encoding. Several networks are developed and analysed to exploit these codes, based around a recurrent version of the non-holographic associati ve memory of Willshaw, et al. The simplest network is shown to have properties similar to those of a Hopfield network, but the storage capacity is shown to be greater, though at a cost of lower signal to noise ratio. Subsequent network additions break each K-from-N pattern into L subsets, each using D-from-N coding, creating cyclic patterns of period L. This step increases the capacity still further but at a cost of lower signal to noise ratio. The use of the network in associating pairs of input patterns with any given output pattern, an architectural requirement, is verified. The use of complex synaptic junctions is investigated as a means to increase storage capacity, to address the stability-plasticity dilemma and to implement the hierarchical aspects of the symbol encoding defined in the architecture. A wide range of options is developed which allow a number of key global parameters to be traded-off. One scheme is analysed and simulated. A final section examines some of the elements that need to be added to our current understanding of neural network-based reasoning systems to make general purpose intelligent systems possible. It is argued that the sections of this work represent pieces of the whole in this regard and that their integration will provide a sound basis for making such systems a reality