Propositional Knowledge Representation Research Articles

Degrees of truthlikeness, independence, equality and order in probabilistic propositional knowledge representation

Abstract Probabilistic propositional knowledge representation, as here conceived, deals with knowledge representation in the form of probabilistic (p-)valuations of the atomic propositions of a propositional language, of probability (p-)distributions over propositional state descriptions and with their relation. The aim of the paper is conceptual: introducing and illustrating a number of related, normalized concepts regarding both valuations and distributions: degrees of truthlikeness, (internal) independence, equality and order. The backbone of these concepts is the relevant sum of absolute distances. All degrees are in the unit interval and will be illustrated at the relevant place by data about the co-morbidity of psychiatric syndromes (Van Loo et al., 2016). In a new section we illustrate all concepts by an example of weather conditions, to be precise: (not) windy, (not) hot, (not) rainy days in March 2023, in De Bilt (NL). We also briefly discuss some other approaches: other distance measures, covariance as a measure of mutual dependence and various degrees of equality. In the final section we enlist several further research questions and issues, among which the question whether this paper is relevant for propositional probability logics. The paper is highly inspired by the rich paper of Gustavo Cevolani and Roberto Festa (2021).

Research Paper on Artificial Intelligence

Artificial Intelligence (AI) has emerged as a transformative force across various sectors, revolutionizing processes, enhancing efficiency, and redefining innovation. This research paper delves into the multifaceted landscape of AI, focusing on its applications, knowledge representation, and implications for innovation. The paper begins by exploring the diverse applications of AI across healthcare, gaming, finance, data security, social media, robotics, and e-commerce. In healthcare, AI aids in diagnosis and patient care, while in gaming, it enables strategic game play and enhances user experience. The finance sector leverages AI for automation, analytics, and algorithmic trading, improving decision-making and customer service. AI also plays a vital role in ensuring data security through advanced detection systems, manages vast social media data for enhanced user engagement, and drives innovation in robotics and e-commerce. Moving forward, the paper delves into the realm of expert systems and knowledge representation, elucidating the role of AI in simulating human expertise and modeling complex information structures. It discusses various aspects of knowledge representation, such as propositional knowledge representation, image retrieval, functional relationships between objects, and class representation formalism, highlighting their significance in developing intelligent systems. Furthermore, the paper examines the integration of AI in maintenance practices, both for tangible systems like engineering workshops and intangible products like data extraction wrappers. It underscores the importance of AI in optimizing operational efficiency, reducing downtime, and ensuring continuous data extraction. Lastly, the paper explores the concept of deep learning as a general- purpose invention, discussing its potential implications for innovation, management, institutions, and policy. It addresses key issues such as the management and organization of innovation, intellectual property rights, competition policy, and the cumulative knowledge production facilitated by deep learning. In conclusion, this research paper provides a comprehensive overview of AI's transformative potential, emphasizing the need for further research and analysis to fully comprehend its impact on society, economy, and innovation.

Complex compound option models – Can practitioners truly operationalize them?

While there is practitioner interest in real options (RO), there are significant difficulties in practitioner use of complicated RO models, such as compound options pricing models (OPMs) of multistage investments. Drawing upon theories of learning and knowledge, we propose a general framework whereby practitioners can successfully operationalize complex OPMs. From an epistemological perspective, most academic articles on OPMs are essentially propositional knowledge representations that attest to model veracity, and they require a deep background in the analytics of options pricing to comprehend the model which many practitioners lack. A key element of our framework is that the propositional knowledge representation of complex OPMs must be accompanied by a layer of abstraction translating the propositional into procedural knowledge for using the model. Secondly, while this layer of abstraction can be embodied in a software tool for using the OPM, this must be transparently done in order to build trust in the software tool. Thirdly, in the tradition of constructivist learning, using the tool must be illustrated in the context of some contemporary business problem. Finally, a continuous engagement loop must be established which makes routine the application of the RO methodology using this tool. We demonstrate these steps in the context of Geske compound option models for multistage investments. We also show how to apply our tool to the major business problem of enterprise integration. This article therefore provides academics with prescription on fostering practitioner embrace of complex OPMs and practitioners with a tool for operationalizing n-fold compound option models.

Connectionist model generation: A first-order approach

Knowledge-based artificial neural networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes as expressed e.g., by means of first-order predicate logic, it is not obvious at all what neural-symbolic systems would look like such that they are truly connectionist, are able to learn, and allow for a declarative reading and logical reasoning at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. We show in this paper how the core method can be used to learn first-order logic programs in a connectionist fashion, such that the trained network is able to do reasoning over the acquired knowledge. We also report on experimental evaluations which show the feasibility of our approach.

Neural representation of verb meaning: an fMRI study.

The neural basis for verb comprehension has proven elusive, in part because of the limited range of verb categories that have been assessed. In the present study, 16 healthy young adults were probed for the meaning associated with verbs of MOTION and verbs of COGNITION. We observed distinct patterns of activation for each verb subcategory: MOTION verbs are associated with recruitment of left ventral temporal-occipital cortex, bilateral prefrontal cortex and caudate, whereas COGNITION verbs are associated with left posterolateral temporal activation. These findings are consistent with the claim that the neural representations of verb subcategories are distinct. Although the "sensory-motor" hypothesis may play a role in explaining activation associated with MOTION verbs, the left posterolateral temporal distribution of cortical activation associated with COGNITION verbs cannot be easily explained by the "sensory-motor" hypothesis. We suggest that left posterolateral temporal activation supports aspects of lexical semantic processing concerned with the neural representation of propositional knowledge contributing to COGNITION verbs.

Space Efficiency of Propositional Knowledge Representation Formalisms

We investigate the space efficiency of a Propositional Knowledge Representation (PKR) formalism. Intuitively, the space efficiency of a formalism F in representing a certain piece of knowledge A, is the size of the shortest formula of F that represents A. In this paper we assume that knowledge is either a set of propositional interpretations (models) or a set of propositional formulae (theorems). We provide a formal way of talking about the relative ability of PKR formalisms to compactly represent a set of models or a set of theorems. We introduce two new compactness measures, the corresponding classes, and show that the relative space efficiency of a PKR formalism in representing models/theorems is directly related to such classes. In particular, we consider formalisms for nonmonotonic reasoning, such as circumscription and default logic, as well as belief revision operators and the stable model semantics for logic programs with negation. One interesting result is that formalisms with the same time complexity do not necessarily belong to the same space efficiency class.

Visual Knowledge Representation of Moving Scenes

A framework for the representation of visual knowledge, with particular attention to the analysis and the representation of scenes with moving persons and objects is described. Our aim is to integrate in a principled way the models developed within the artificial vision community, and the propositional knowledge representation systems developed within symbolic AI. We claim that this integration requires the introduction of a missing link between these two classes of representations. In our model, such a role is played by the notion of conceptual space (CS), a representation in which information is characterised in terms of a metric space. We illustrate our proposal by referring to an experimental setup, based on a vision system that takes in input real images of moving people.

An Electronic Study Form to Support Collaborating Agents in the Management of Clinical Knowledge

When dealing with biological organisms, one has to take into account some peculiarities which significantly affect the representation of knowledge about them. These are complemented by the limitations in the representation of propositional knowledge, i.e. the majority of clinical knowledge, by artificial agents. Thus, the opportunities to automate the management of clinical knowledge are widely restricted to closed contexts and to procedural knowledge. Therefore, in dynamic and complex real-world settings such as health care provision to HIV-infected patients human and artificial agents must collaborate in order to optimize the time/quality antinomy of services provided. If applied to the implementation level, the overall requirement ensues that the language used to model clinical contexts should be both human- and machine-interpretable. The eXtensible Markup Language (XML), which is used to develop an electronic study form, is evaluated against this requirement, and its contribution to collaboration of human and artificial agents in the management of clinical knowledge is analyzed.

Is intractability of nonmonotonic reasoning a real drawback?

Several studies about computational complexity of nonmonotonic reasoning (NMR) showed that nonmonotonic inference is significantly harder than classical, monotonic inference. This contrasts with the general idea that NMR can be used to make knowledge representation and reasoning simpler, not harder. In this paper we show that, to some extent, NMR fulfills the representation goal. In particular, we prove that nonmonotonic formalisms such as circumscription and default logic allow for a much more compact and natural representation of propositional knowledge than propositional calculus. Proofs are based on a suitable definition of a compilable inference problem, and on non-uniform complexity classes. Some results about intractability of circumscription and default logic can therefore be interpreted as the price one has to pay for having such an extra-compact representation. On the other hand, intractability of inference and compactness of representation are not equivalent notions: we exhibit intractable nonmonotonic formalisms whose nonmonotonic assumptions are representable by few propositional formulae. Finally, sometimes NMR really makes reasoning simpler. We present prototypical scenarios where closed-world reasoning and well-founded semantics account for a faster, complete and unsound approximation of classical reasoning.

Special Issue on Propositional Knowledge Representation

Special Issue on Propositional Knowledge Representation

The ECO family

This paper presents an overview of the ECO (English COnversational System) family formalism of semantic network. In the paper, we describe the components of our semantic network, discussing its suitability as a representation of propositional knowledge. The use of our semantic network as a uniform representation mediating between specialised representations appropriate to particular task domains (e.g., understanding natural languages, etc.) is discussed. We motivate and explain a comprehensive network formalism. Special problems with respect to the use of logical connectives, quantifiers, descriptions, modalities, and certain other constructions that fail in conventional semantic networks, are systematically resolved with extensions to conventional network notations. The representation harmonizes with linear one-dimensional logical notations, illustrating the close kinship of the two notations. This kinship supports the claim that networks have inherited formal interpretability from logical notations. Several issues of network form and content, which are more fundamental than the choice of a network syntax, are addressed. These issues are: (i) primitive versus nonprimitive representations; (ii) the separation of propositional content of text from pragmatic aspects; and (iii) network normal form versus ad hoc systems. The design of computer systems for specific tasks depends in part on early commitments to these issues. The succinctness, clarity, and intuitive nature of semantic networks argues in their favour if only for purely methodological advantages. Semantic networks are readable; they suggest procedures for comprehension and inference, and the computer data structures which they resemble. Examples will demonstrate how associative processing algorithms and complex pattern matching operations are readily identifiable using networks. These examples are given in the context of natural language understanding utilizing networks in a state-based conceptual representation. We discuss how to superimpose organisational strategies into the network representations, beginning with the representation of lexical information and extending to the superimposition of topical organisations in the knowledge base. Several special purpose inference mechanisms extend the topical organisation we superimpose on concepts to aid retrieval of other types of information about concepts. The use of networks is assessed and promising areas for future research are described.