An Artificial Network Simulating Cause-to-Effect Reasoning: Cancellation Interactions and Numerical Studies

During the last few decades, a variety of models have been proposed to address causal reasoning (known also as abduction); most of these dealt with a probabilistic or a logical framework. Recently, a few models have been proposed within a neural framework. The investigation of neural approaches is mainly motivated by the computational burden of the causal reasoning task and by the satisfactory results given by neural networks in solving hard problems in general. A particular class of causal reasoning that raises several difficulties is the cancellation class. From an abstract point of view, cancellation occurs when two causes (hypotheses) cancel each other's explanation capabilities with respect to a given effect (observation). The present work is twofold. First, we extend an existing neural model to handle cancellation interactions. Second, we test the model on a large database and propose objective criteria to quantitatively evaluate the scenarios (explanations) produced. Simulation results show good performance and stability of the model. Received 17 November 1999 / Revised 12 May 2000 / Accepted in revised form 16 June 2000     

Adaptive fuzzy spiking neural P systems for fuzzy inference and learning

Fuzzy spiking neural P systems (in short, FSN P systems) are a novel class of distributed parallel computing models, which can model fuzzy production rules and apply their dynamic firing mechanism to achieve fuzzy reasoning. However, these systems lack adaptive/learning ability. Addressing this problem, a class of FSN P systems are proposed by introducing some new features, called adaptive fuzzy spiking neural P systems (in short, AFSN P systems). AFSN P systems not only can model weighted fuzzy production rules in fuzzy knowledge base but also can perform dynamically fuzzy reasoning. It is important to note that AFSN P systems have learning ability like neural networks. Based on neuron's firing mechanisms, a fuzzy reasoning algorithm and a learning algorithm are developed. Moreover, an example is included to illustrate the learning ability of AFSN P systems.     

Axiomatic treatment of processes with shared variables revisited

The aim of this paper is to develop simple and practically useful formalisms for reasoning about processes with shared variables. Our approach is based on the axiomatic system described by Neelam Soundararajan. In contrast to that work, our formalism is first derived from a model; this guarantees soundness and completeness of the formal proof system, with respect to the model. As an additional advantage the rules become simpler than those of Soundararajan; in particular, the local assertions may freely refer to shared variables; and we remove the explicit use of the compatibility predicate.Next we augment the formalism by allowing global invariants, which may refer to shared variables (including shared histories), but with a different semantics than in the local assertions. The augmented system makes reasoning simpler in the sense that reasoning about the past is replaced by reasoning about the present. Finally we suggest a sufficiently complete set of mythical (auxiliary) variables free from embedded program structure. We demonstrate our formalism on some examples.Dedicated to the memory of Jan Helge DÆhlin, 1959–1989     

Reasoning on temporal class diagrams: Undecidability results

This paper introduces a temporal class diagram language useful to model temporal varying data. The atemporal portion of the language contains the core constructors available in both EER diagrams and UML class diagrams. The temporal part of the language is able to distinguish between temporal and atemporal constructs, and it has the ability to represent dynamic constraints between classes. The language is characterized by a model-theoretic (temporal) semantics. Reasoning services as logical implication and satisfiability are also defined. We show that reasoning on finite models is different from reasoning on unrestricted ones. Then, we prove that reasoning on temporal class diagrams is an undecidable problem on both unrestricted models and on finite ones.     

Meta-path reasoning of knowledge graph for commonsense question answering

Commonsense question answering (CQA) requires understanding and reasoning over QA context and related commonsense knowledge, such as a structured Knowledge Graph (KG). Existing studies combine language models and graph neural networks to model inference. However, traditional knowledge graph are mostly concept-based, ignoring direct path evidence necessary for accurate reasoning. In this paper, we propose MRGNN (Meta-path Reasoning Graph Neural Network), a novel model that comprehensively captures sequential semantic information from concepts and paths. In MRGNN, meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths simultaneously. We conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets, showing the effectiveness of MRGNN. Also, we conduct further ablation experiments and explain the reasoning behavior through the case study.     

A Fuzzy Inference Network Model for Search Strategy Using Neural Logic Network

In this paper, a fuzzy inference network model for search strategy using neural logic network is presented. The model describes search strategy, and neural logic network is used to search. Fuzzy logic can bring about appropriate inference results by ignoring some information in the reasoning process. Neural logic networks are powerful tools for the reasoning process but not appropriate for the logical reasoning. To model human knowledge, besides the reasoning process capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct a fuzzy inference network model based on the neural logic network, extending the existing rule inference network. And the traditional propagation rule is modified.     

Modeling default reasoning using defaults

User modeling research can benefit from formal automated reasoning tools. However existing formal tools may need to be modified to suit the needs of user modeling. Theorist is a simple framework for default reasoning. It can be used as a tool for building and maintaining a user model, and as a model of a user's default reasoning. To apply Theorist to both tasks, we develop Nested Theorist (NT), a simple tool based on Theorist that allows default reasoning on arbitrarily-many levels. We extend NT in two ways: we allow prioritized defaults, and we allow reasoning about agents with limited reasoning capabilities. This paper focusses on applications, and uses wide-ranging examples from user-modeling literature to illustrate the usefulness of the tools presented.     

To hop or not,that is the question: Towards effective multi-hop reasoning over knowledge graphs

With the proliferation of large-scale knowledge graphs (KGs), multi-hop knowledge graph reasoning has been a capstone that enables machines to be able to handle intelligent tasks, especially where some explicit reasoning path is appreciated for decision making. To train a KG reasoner, supervised learning-based methods suffer from false-negative issues, i.e., unseen paths during training are not to be found in prediction; in contrast, reinforcement learning (RL)-based methods do not require labeled paths, and can explore to cover many appropriate reasoning paths. In this connection, efforts have been dedicated to investigating several RL formulations for multi-hop KG reasoning. Particularly, current RL-based methods generate rewards at the very end of the reasoning process, due to which short paths of hops less than a given threshold are likely to be overlooked, and the overall performance is impaired. To address the problem, we propose RL-MHR, a revised RL formulation of multi-hop KG reasoning that is characterized by two novel designs—the stop signal and the worth-trying signal. The stop signal instructs the agent of RL to stay at the entity after finding the answer, preventing from hopping further even if the threshold is not reached; meanwhile, the worth-trying signal encourages the agent to try to learn some partial patterns from the paths that fail to lead to the answer. To validate the design of our model RL-MHR, comprehensive experiments are carried out on three benchmark knowledge graphs, and the results and analysis suggest the superiority of RL-MHR over state-of-the-art methods.

     

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and complete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to generate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach.     

Reasoning in inconsistent knowledge bases

Databases and knowledge bases could be inconsistent in many ways. For example, during the construction of an expert system, we may consult many different experts. Each expert may provide us with a group of rules and facts which are self-consistent. However, when we coalesce the facts and rules provided by these different experts, inconsistency may arise. Alternatively, knowledge bases may be inconsistent due to the presence of some erroneous information. Thus, a framework for reasoning about knowledge bases that contain inconsistent information is necessary. However, existing frameworks for reasoning with inconsistency do not support reasoning by cases and reasoning with the law of excluded middle (“everything is either true or false”). In this paper, we show how reasoning with cases, and reasoning with the law of excluded middle may be captured. We develop a declarative and operational semantics for knowledge bases that are possibly inconsistent. We compare and contrast our work with work on explicit and non-monotonic modes of negation in logic programs and suggest under what circumstances one framework may be preferred over another     

A case-based reasoning approach for building a decision model

A methodology based on case-based reasoning is proposed to build a topological-level influence diagram. It is then applied to a project proposal review process. The formulation of decision problems requires much time and effort, and the resulting model, such as an influence diagram, is applicable only to one specific problem. However, some prior knowledge from the experience in modeling influence diagrams can be utilized to resolve other similar decision problems. The basic idea of case-based reasoning is that humans reuse the problem-solving experience to solve new problems.
In this paper, we suggest case-based decision class analysis (CB-DCA), a methodology based on case-based reasoning, to build an influence diagram. CB-DCA is composed of a case retrieval procedure and an adaptation procedure. Two measures are suggested for the retrieval procedure, one a fitting ratio and the other a garbage ratio. The adaptation procedure is based on decision-analytic knowledge and decision participants' domain-specific knowledge. Our proposed methodology has been applied to an environmental review process in which decision-makers need decision models to decide whether a project proposal is accepted or not. Experimental results show that our methodology for decision class analysis provides decision-makers with robust knowledge-based support.     

综合推理与智能书法创作

In this paper, we address the demanding task of developing intelligent systems equipped with machine creativity that can perform design tasks automatically. The main challenge is how to model human beings' creativity mathematically and mimic such creativity computationally. We propose a ``synthesis reasoning model" as the underlying mechanism to simulate human beings' creative thinking when they are handling design tasks. We present the theory of the synthesis reasoning model, and the detailed procedure of designing an intelligent system based on the model. We offer a case study of an intelligent Chinese calligraphy generation system which we have developed. Based on implementation experiences of the calligraphy generation system as well as a few other systems for solving real-world problems, we suggest a generic methodology for constructing intelligent systems using the synthesis reasoning model.     

Hybrid knowledge bases

Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics     

Reasoning about reasoning in a meta-level architecture

In this paper we discuss reasoning about reasoning in a multiple agent scenario. We consider agents that are perfect reasoners, loyal, and that can take advantage of both the knowledge and ignorance of other agents. The knowledge representation formalism we use is (full) first order predicate calculus, where different agents are represented by different theories, and reasoning about reasoning is realized via a meta-level representation of knowledge and reasoning. The framework we provide is pretty general: we illustrate it by showing a machine checked solution to the three wisemen puzzle. The agents' knowledge is organized into units: the agent's own knowledge about the world and its knowledge about other agents are units containing object-level knowledge; a unit containing meta-level knowledge embodies the reasoning about reasoning and realizes the link among units. In the paper we illustrate the meta-level architecture we propose for problem solving in a multi-agent scenario; we discuss our approach in relation to the modal one and we compare it with other meta-level architectures based on logic. Finally, we look at a class of applications that can be effectively modeled by exploiting the meta-level approach to reasoning about knowledge and reasoning.     

Covering photo-realistic properties of outdoor components with the effects of sky color in mixed reality

Outdoor rendering is an attractive topic in computer graphics. In this paper our main concern is to reveal the interaction between sky color and virtual objects in Mixed Reality environments. Although registration and tracking are two of the main issues in building effective Augmented Reality (AR) systems the creation of more realistic virtual objects indistinguishable from their real-world counterparts is our target which is currently the ultimate goal in AR. Two classes of sky color generation are employed to reveal the outdoor-element interaction. Virtual Sky Modelling (VSM) based on the Perez Model is capable of generating the sky color in a specific location, date and time. The second technique is to generate a virtual model based on the real image of the sky which is called in this paper Real Sky Modelling (RSM). Subsequently, preprocessing of the sky color bleeding is based on the radiosity technique to give the sky color effect to the virtual objects as well as the real ones. Through designing a test AR set-up and applying software and hardware the goal of a robust generation of realistic virtual objects with effect of sky color is achieved.     

一种基于模糊加权型推理法的模糊神经网络

本文在Ｍａｍｄａｎｉ模糊推理法的基础上,给出了改进的模糊加权型推理法－广义模糊加权型推理法。     

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.     

A case-study in algebraic manipulation using mechanized reasoning tools

In this article, two different mechanized reasoning tools (Coq and Isabelle/HOL) are used in order to prove some basic but significant properties extracted from a symbolic computation system called Kenzo. In particular, we focused on a property called ‘cancellation theorem’, which can be applied to the proof of the idempotence property of a differential morphism. This result is used as a case-study to compare the capabilities and styles of the two proof assistants. The conclusion of our comparison is that both tools are adequate to solve the case-study presented in this article in a rather similar way but depending on their specific styles. This research is part of a more general project devoted to increase the reliability of computer algebra systems for calculations in algebraic topology.     

Self-Tuning of the Fuzzy Inference Rule by Integrated Method

In the fuzzy reasoning model, the fuzzy relation matrix, determined by a human expert according to experience, plays an important role, but may be difficult to extract optimally from an expert, particularly as the system increases in complexity. Moreover, a change in the fuzzy membership function may alter the performance of the fuzzy system significantly. Therefore, in this paper, the genetic algorithm is to be incorporated in the context fuzzy reasoning model in the loop whose function is to search for optimal fuzzy relation matrix and fuzzy membership functions simultaneously. In addition, the genetic algorithm used in this paper is supplemented by a local fine-tuning mechanism with executing the gradient descent genetic operator.