A connectionist processing metaphor for diagnostic reasoning

This article describes LIBRA/Dx, a competition-based parallel activation model for diagnostic reasoning. Within a causal network, the model uses a neurally inspired processing paradigm to generate the most plausible explanation for a set of observed manifestations. the model was built using LIBRA: a domain-independent parallel activation network generator, that can be used to build network models with processing paradigms that are tailored to the specifics of an application domain. the underlying theory postulates that by simultaneously satisfying multiple constraints that may exist locally among domain concepts in a causal network (e.g., among disorders, syndromes, manifestations, etc.) it is possible to construct a plausible global explanation for a set of observed signs and symptoms. the proposed processing paradigm which uses an associative network of concepts to represent domain knowledge, lends itself to the kind of interactive processing that is necessary to capture the generative capacity of human diagnostic ability in novel situations. LIBRA/Dx offers a new approach to modeling a higher cognitive process: diagnostic reasoning, specifically in terms of the time-course of processing and the nature of knowledge representation. It further contributes to our current understanding of the phenomena of human cognition, which have eluded successful explication in conventional computational formalisms.     

A temporal revision model for reasoning about world change

Revision can be seen as any operation which turns a cognitive state CSt into a subsequent cognitive state CSt'. Two kinds of change can be considered: in the “belief change” case, the cognitive states represent beliefs on a world; they are revised in response to the getting of new information about a static world. In the “world change” case, the cognitive states represent known facts on a real world; they are revised in response to change in this dynamic world. We focus in the following on world change case and propose a way to keep up to date with a dynamic world. Reasoning about change requires predicting how the world will change along time. In absence of a predictive model of evolution, the commonsense law of inertia has been currently used and justifies the minimal change approach to the frame problem. We propose here to use an explicit transition model, which will be used as a predictive evolution model. Dean and Kanazawa propose to use a probabilistic model of persistence and causation. We propose in this paper to use a symbolic model of transition by directly encoding expectations. In the first two sections, we describe the formalism that we propose to explicitly encode the transition model and its axiomatisation. We give then a formal definition of the revision operation using a transition model and discuss what can be a contraction operation in the context of world change. an illustrative example is presented and in the last section, our approach is compared to other related works. © 1994 John Wiley & Sons, Inc.     

A connectionist approach for similarity assessment in case-based reasoning systems

Case-Based Reasoning (CBR) systems support ill-structured decision making. In ill-structured decision environments, decision makers (DMs) differ in their problem solving approaches. As a result, CBR systems would be more useful if they were able to adapt to the idiosyncrasies of individual decision makers. Existing implementations of CBR systems have been mainly symbolic, and symbolic CBR systems are unable to adapt to the preferences of decision makers (i.e., they are static). Retrieval of appropriate previous cases is critical to the success of a CBR system. Widely used symbolic retrieval functions, such as nearest-neighbor matching, assume independence of attributes and require specification of their importance for matching. To ameliorate these deficiencies connectionist systems have been proposed. However, these systems are limited in their ability to adapt and grow. To overcome this limitation, we propose a distributed connectionist-symbolic architecture that adapts to the preferences of a decision maker and that, additionally, ameliorates the limitations of symbolic matching. The proposed architecture uses a supervised learning technique to acquire the matching knowledge. The architecture allows the growth of a case base without the involvement of a knowledge engineer. Empirical investigation of the proposed architecture in an ill-structured diagnostic decision environment demonstrated a superior retrieval performance when compared to the nearest-neighbor matching function.     

A computational architecture for heterogeneous reasoning

In this paper we describe a computational architecture for applications that support heterogeneous reasoning. Heterogeneous reasoning is, in its most general form, reasoning that employs representations drawn from multiple representational forms. Of particular importance, and the principal focus of the architecture, is heterogeneous reasoning which employs one or more forms of graphical representation, perhaps in combination with sentences (of English or another language, whether natural or scientific). Graphical representations include diagrams, pictures, layouts, blueprints, flowcharts, graphs, maps, tables, spreadsheets, animations, video, and 3D models. By ‘an application that supports heterogeneous reasoning’ we mean an application that allows users to construct, record, edit, and replay a process of reasoning using multiple representations so that the structure of the reasoning is maintained and the informational dependencies and justifications of the individual steps of the reasoning can be recorded.     

A computational model of reasoning from the clinical literature

This paper explores the premise that a formalized representation of empirical studies can play a central role in computer-based decision support. The specific motivations underlying this research include the following propositions: Reasoning from experimental evidence contained in the clinical literature is central to the decisions physicians make in patient care. A computational model, based upon a declarative representation for published reports of clinical studies, can drive a computer program that selectively tailors knowledge of the clinical literature as it is applied to a particular case. The development of such a computational model is an important first step toward filling a void in computer-based decision support systems. Furthermore, the model may help us better understand the general principles of reasoning from experimental evidence both in medicine and other domains. Roundsman is a developmental computer system which draws upon structured representations of the clinical literature in order to critique plans for the management of primary breast cancer. Roundsman is able to produce patient-specific analyses of breast cancer management options based on the 24 clinical studies currently encoded in its knowledge base. The Roundsman system is a first step in exploring how the computer can help to bring a critical analysis of the relevant literature to the physician, structured around a particular patient and treatment decision.     

A Comparative Study of Default Reasoning and Epistemic Processes

A comparative study between the theories of default reasoning and open logic is given.Some concepts of open logic,such as new premises,rejections by facts,reconstructions ,epistemic processes,and its limit are introduced to describe th evolution of hypotheses.An improved version of the limit theorem is given and proved.A model-theoretic interpretation of the closed normal defaults is given using the above concepts and the corresponding completeness is proved.Any extension of a closed normal default theory is proved to be the linit of a δ-partial increasing epistemic process of that theory,and vice versa.It is proved that there exist two distinct extensions of a closed normal default theory iff there is an δ-non-monotonic epistemic process of that theory.The completeness of Reiter‘s proof is also given and proved,in terms of the epistemic processes.Finally,the work is compared with Gaerdenfors‘s theory of knowledge in flux.     

Realistic model for temporal reasoning in real-time patient monitoring

Time is a central factor in patient monitoring. Introduction of domain-dependent knowledge is essential to ensure efficiency of time managers, especially when embedded into systems that interact with the real world. We present a realistic temporal reasoning model based on two basic cognitive mechanisms: aggregation of similar observed situations and forgetting of non-relevant information. We describe in detail how we represented the proposed model and how, by refinement of domain-independent temporal entities and inferences, we added domain specific knowledge to manage a clinical therapy. The model allows clinical observations to be incrementally interpreted as they are acquired by an intelligent system, mainly reactive in its reasoning, for the management of patients receiving respiratory support.     

The epistemic basis of defeasible reasoning

This article argues that: (i) Defeasible reasoning is the use of distinctive procedures for belief revision when new evidence or new authoritative judgment is interpolated into a system of beliefs about an application domain. (ii) These procedures can be explicated and implemented using standard higher-order logic combined with epistemic assumptions about the system of beliefs. The procedures mentioned in (i) depend on the explication in (ii), which is largely described in terms of a Prolog program, EVID, which implements a system for interactive, defeasible reasoning when combined with an application knowledge base. It is shown that defeasible reasoning depends on a meta-level Closed World Assumption applied to the relationship between supporting evidence and a defeasible conclusion based on this evidence. Thesis (i) is then further defended by showing that the EVID explication of defeasible reasoning has sufficient representational power to cover a wide variety of practical applications of defeasible reasoning, especially in the context of decision making.     

A connectionist inference model for pattern-directed knowledge representation

In this paper we propose a connectionist model for variable binding. The model is topology dependent on the graph it builds based on the predicates available. The irregular connections between perceptron-like assemblies facilitate forward and backward chaining. The model treats the symbolic data as a sequence and represents the training set as a partially connected network using basic set and graph theory to form the internal representation. Inference is achieved by opportunistic reasoning via the bidirectional connections. Consequently, such activity stabilizes to a multigraph. This multigraph is composed of isomorphic subgraphs which all represent solutions to the query made. Such a model has a number of advantages over other methods in that irrelevant connections are avoided by superimposing positionally dependent sub-structures that are identical, variable binding can be encoded and multiple solutions can be extracted simultaneously. The model also has the ability to adapt its existing architecture when presented with new clauses and therefore add new relationships/rules to the model explicitly; this is done by some partial retraining of the network due to the superimposition properties.     

Advanced computational reasoning based on the NKRL conceptual model

After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of “rules” – e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWL – this paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL’s methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich non-fictional ‘narrative’ data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between “plain/static” and “structured/dynamic” knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of “functional roles” – associated with the possibility of making use of n-ary structures – allows us to build up highly ‘expressive’ rules whose “atoms” can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, “functional roles” are primitive symbols interpreted as “relations” – like “subject”, “object”, “source”, “beneficiary”, etc. – that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the “semantic roles” that are equated to ordinary concepts like “student”, to be inserted into the “non-sortal” (no direct instances) branch of a traditional ontology.     

A connectionist model for category perception: theory andimplementation

A connectionist model for learning and recognizing objects (or object classes) is presented. The learning and recognition system uses confidence values for the presence of a feature. The network can recognize multiple objects simultaneously when the corresponding overlapped feature train is presented at the input. An error function is defined, and it is minimized for obtaining the optimal set of object classes. The model is capable of learning each individual object in the supervised mode. The theory of learning is developed based on some probabilistic measures. Experimental results are presented. The model can be applied for the detection of multiple objects occluding each other.     

A connectionist model for corner detection in binary and grayimages

For a given binary/gray image, each pixel in the image is assigned with some initial cornerity (our measurable quantity) which is a vector representing the direction and strength of the corner. These cornerities are then mapped onto a neural-network model which is essentially designed as a cooperative computational framework. The cornerity at each pixel is updated depending on the neighborhood information. After the network dynamics settles to stable state, the dominant points are obtained by finding out the local maxima in the cornerities. Theoretical investigations are made to ensure the stability and convergence of the network. It is found that the network is able to detect corner points: even in the noisy images and for open object boundaries. The dynamics of the network is extended to accept the edge information from gray images as well. The effectiveness of the model is experimentally demonstrated in synthetic and real-life binary and gray images.     

Quantified epistemic logics for reasoning about knowledge in multi-agent systems

We introduce quantified interpreted systems, a semantics to reason about knowledge in multi-agent systems in a first-order setting. Quantified interpreted systems may be used to interpret a variety of first-order modal epistemic languages with global and local terms, quantifiers, and individual and distributed knowledge operators for the agents in the system. We define first-order modal axiomatisations for different settings, and show that they are sound and complete with respect to the corresponding semantical classes.The expressibility potential of the formalism is explored by analysing two MAS scenarios: an infinite version of the muddy children problem, a typical epistemic puzzle, and a version of the battlefield game. Furthermore, we apply the theoretical results here presented to the analysis of message passing systems [R. Fagin, J. Halpern, Y. Moses, M. Vardi, Reasoning about Knowledge, MIT Press, 1995; L. Lamport, Time, clocks, and the ordering of events in a distributed system, Communication of the ACM 21 (7) (1978) 558–565], and compare the results obtained to their propositional counterparts. By doing so we find that key known meta-theorems of the propositional case can be expressed as validities on the corresponding class of quantified interpreted systems.     

Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. We claim that a promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. We are accordingly modifying our current neural net system (Conposit), which performs standard rule-based reasoning, into a massively parallel case-based reasoning version.     

Quantitative temporal reasoning

A substantially large class of programs operate in distributed and real-time environments, and an integral part of their correctness specification requires the expression of time-critical properties that relate the occurrence of events of the system. We focus on the formal specification and reasoning about the correctness of such programs. We propose a system of temporal logic, RTCTL (Real-Time Computation Tree Logic), that allows the melding of qualitative temporal assertions together with real-time constraints to permit specification and reasoning at the twin levels of abstraction: qualitative and quantitative. We argue that many practically useful correctness properties of temporal systems, which need to express timing as an essential part of their functionality requirements, can be expressed in RTCTL. We develop a model-checking algorithm for RTCTL whose complexity is linear in the size of the RTCTL specification formula and in the size of the structure. We also present an essentially optimal, exponential time tableau-based decision procedure for the satisfiability of RTCTL formulae. Finally, we consider several variants and extensions of RTCTL for real-time reasoning.The work of E.A. Emerson was supported in part by NSF grant DCR-8511354, ONR URI contract N00014-86-K-0763, and Netherlands NWO grant nf-3/nfb 62-500. The work of A.K.Mok was supported in part by ONR Grant number N00014-89-J-1472 and Texas Advanced Technology Program Grant 003658-250. A summary of these results was presented at the Workshop on Automatic Verification Methods for Finite State Systems, Grenoble, France, June 12–14, 1989.     

A unified model for abduction-based reasoning

In the last decade, abduction has been a very active research area. This has resulted in a variety of models mechanizing abduction, namely within a probabilistic or logical framework. Recently, a few abductive models have been proposed within a neural framework. Unfortunately, these neural/probablistic/logical-based models cannot address complex abduction problems. In this paper, we propose a new extended neural-based model to deal with abduction problems which could be monotonic, open, and incompatible     

A causal model for diagnostic reasoning

Up to now,there have many methods for knowledge representation and reasoning in causal networks,but few of them include the research on the coactions of nodes.In practice,ignoring these coactions may influence the accureacy of reasoning and even give rise to incorrect reasoning.In this paper,based on multilayer causal networks.the definitions on coaction nodes are given to construct a new causal network called Coaction Causal Network,which serves to construct a model of nerual network for diagnosis followed by fuzzy reasoning,and then the activation rules are given and neural computing methods are used to finish the diagnostic reasoning,These methods are proved in theory and a method of computing the number of solutions for the diagnostic reasoning is given.Finally,the experiments and the conclusions are presented.     

Fuzzy temporal reasoning for process supervision

Abstract: Process supervision consists of following the temporal evolution (change) of process behaviours. This task has usually been performed based on the knowledge and experience of domain experts and operators. Actually, these experts and operators almost always express their experience and knowledge about process evolution in an imprecise, fuzzy and vague way. A good supervision system should be capable of dealing at once with two different kinds of knowledge: time and uncertainty.
For many years, time and uncertainty have been two of the most important topics in Artificial Intelligence research and applications. Many approaches have been proposed to deal with either one or the other. Among the various approaches for time, reified logic has been considered as the most influent one. Possibilistic logic, on the other hand, has shown its ability to handle uncertain knowledge and information. This paper describes an approach for managing temporal uncertainty based on fuzzy logic and possibility theory. A fuzzy temporal expert system shell has been developed to perform process supervision tasks.     

On point-duration networks for temporal reasoning

We present here a point-duration network formalism which extends the point algebra model to include additional variables that represent durations between points of time. Thereafter the new qualitative model is enlarged for allowing unary metric constraints on points and durations, subsuming in this way several point-based approaches to temporal reasoning. We deal with some reasoning tasks within the new models and we show that the main problem, deciding consistency, is NP-complete. However, tractable special cases are identified and we show efficient algorithms for checking consistency, finding a solution and obtaining the minimal network.