Jingde Cheng

© 2015 Macmillan Publishers Limited. All rights reserved.One of the primary goals of nuclear physics is to understand the force between nucleons, which is a necessary step for understanding the structure of nuclei and how nuclei interact with each other. Rutherford discovered the atomic nucleus in 1911, and the large body of knowledge about the nuclear force that has since been acquired was derived from studies made on nucleons or nuclei. Although antinuclei up to antihelium-4 have been discovered and their masses measured, little is known directly about the nuclear force between antinucleons. Here, we study antiproton pair correlations among data collected by the STAR experiment at the Relativistic Heavy Ion Collider, where gold ions are collided with a centre-of-mass energy of 200 gigaelectronvolts per nucleon pair. Antiprotons are abundantly produced in such collisions, thus making it feasible to study details of the antiproton-antiproton interaction. By applying a technique similar to Hanbury Brown and Twiss intensity interferometry, we show that the force between two antiprotons is attractive. In addition, we report two key parameters that characterize the corresponding strong interaction: the scattering length and the effective range of the interaction. Our measured parameters are consistent within errors with the corresponding values for proton-proton interactions. Our results provide direct information on the interaction between two antiprotons, one of the simplest systems of antinucleons, and so are fundamental to understanding the structure of more-complex antinuclei and their properties.

In order to provide scientists with a computational methodology and some computational tools to program their epistemic processes in scientific discovery, we are establishing a novel programming paradigm, named ‘Epistemic Programming’, which regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming. This paper presents our fundamental observations and assumptions on scientific discovery processes and their automation, research problems on modeling, automating, and programming epistemic processes, and an outline of our research project of Epistemic Programming.

In many applications in computer science and artificial intelligence, in order to represent, specify, verify, and reason about various objects and relationships among them, we often need a right fundamental logic system to provide us with a criterion of logical validity for reasoning as well as a formal representation and specification language. Although different applications may require different logic systems, the fundamental logics must be able to underlie truth-preserving and relevant reasoning in the sense of conditional, ampliative reasoning, paracomplete reasoning, and paraconsistent reasoning. Based on our experiences, this paper shows that strong relevant logic can be used as the universal basis to construct various applied logics to satisfy the requirements. The paper discusses why any of the classical mathematical logic, its various classical conservative extensions, and its non-classical alternatives is not a suitable candidate for the universal basis to construct various applied logics, shows that strong relevant logic is a more hopeful candidate for the purpose, and presents our experiences on constructions of temporal relevant logics, deontic relevant logics, spatial relevant logics, and spatial-temporal relevant logics.

Scientific discovery has traditionally been regarded as one of the most sophisticated manifestations of human intelligence. While computer science has developed numerous programming paradigms for data processing, symbolic manipulation, numerical computation, and logical deduction, comparatively little attention has been devoted to the problem of programming the processes through which new knowledge is created. In the mid-1990s, Jingde Cheng initiated a research program known as “Epistemic Programming,” which explicitly proposed a programming paradigm for scientific discovery and knowledge creation. Unlike traditional programming paradigms, Epistemic Programming regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computation, and regards epistemic processes as the subject of programming. Over the subsequent two decades, the research evolved into a broader framework encompassing logical foundations, computational models of scientific discovery, programming methodologies, programming languages, truth maintenance mechanisms, and automated theorem finding systems. This paper reviews the origins, theoretical foundations, major developments, and intellectual significance of Epistemic Programming. It argues that the framework represents one of the earliest systematic attempts to establish a computational theory of scientific discovery and knowledge creation. Particular attention is paid to its conception of scientific discovery as an epistemic process, its use of strong relevant logic as a logical foundation, its reinterpretation of computation as epistemic transformation, and its relevance to contemporary research on AI scientists and autonomous scientific discovery. The paper further argues that Epistemic Programming may be viewed as a significant conceptual shift in the history of programming paradigms, extending the focus of programming from computation and deduction toward knowledge creation itself.

The problem of automated theorem finding is one of the 33 basic research problems in automated reasoning which was originally proposed by Wos in 1988, and it is still an open problem. To solve the problem, an approach of forward deduction based on the strong relevant logics was proposed. Following the approach, this paper presents a systematic methodology for automated theorem finding. To show the effectiveness of our methodology, the paper presents two case studies, one is automated theorem finding in NBG set theory and the other is automated theorem finding in Peano’s arithmetic. Some known theorems have been found in our case studies.

This paper proposes a new relevant logic B+⊓⊔, which is obtained by adding two binary connectives, intensional conjunction ⊓ and intensional disjunction ⊔, to Meyer–Routley minimal positive relevant logic B+, where ⊓ and ⊔ are weaker than fusion ◦ and fission +, respectively. We give Kripke-style semantics for B+⊓⊔, with →, ⊓ and ⊔ modelled by ternary relations. We prove the soundness and completeness of the proposed semantics. A number of axiomatic extensions of B+⊓⊔, including negation-extensions, are also considered, together with the corresponding semantic conditions required for soundness and completeness to be maintained.

Traditional information systems are passive, i.e., data or knowledge is created, retrieved, modified, updated, and deleted only in response to operations issued by users or application programs, and the systems only can execute queries or transactions explicitly submitted by users or application programs but have no ability to do something actively by themselves. Unlike a traditional information system serving just as a storehouse of data or knowledge and working passively according to queries or transactions explicitly issued by users and application programs, an autonomous evolutionary information system serves as an autonomous and evolutionary partner of its users that discovers new knowledge from its database or knowledge-base autonomously, cooperates with its users in solving problems actively by providing the users with advices, and has a certain mechanism to improve its own state of "knowing" and ability of "working". This paper seminally defines what is an autonomous evolutionary information system, explain why autonomous evolutionary information systems are needed, and presents some new issues, fundamental considerations, and research directions in design and development of autonomous evolutionary information systems.

In order to provide scientists with a computational methodology and some computational tools to program their epistemic processes in scientific discovery, we are establishing a novel programming paradigm, named ‘Epistemic Programming’, which regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming. This paper seminally presents our fundamental observations and assumptions on scientific discovery processes and their automation, research problems on modeling, automating, and programming epistemic processes, and an outline of our research project of Epistemic Programming.

Recent advances in large language models (LLMs) have led to widespread claims that these systems exhibit increasingly sophisticated “reasoning” abilities. Contemporary LLM research focuses primarily on the behavioral success of reasoning-related tasks while paying comparatively little attention to the logical correctness/validity of the reasoning processes involved. Evaluations of mathematical problem solving, planning, scientific question answering, and chain-of-thought prompting have reinforced the perception that reasoning emerges from scale. However, from the viewpoint of Philosophy of Logic, two important conceptual issues remain insufficiently examined: First, the nature of “reasoning”, what exactly is “reasoning”? Second, the criteria for “reasoning correctness”, what exactly is “correct reasoning” and how evaluate “reasoning correctness”? Drawing on insights from Logic and Philosophy of Logic, this paper argues the above two important conceptual issues. We provide a definition of “reasoning” and, based on this definition, clearly distinguish between “reasoning correctness” and “reasoning appearance.” We point out that generating seemingly reasonable reasoning trajectories and intermediate steps, and even correct results do not prove the logical correctness/validity of the underlying reasoning process. We further argue that correctness should be treated as an independent dimension in the evaluation of any machine reasoning. Future progress may require explicit inferential evaluation criteria in addition to benchmark accuracy. The paper proposes a new correctness-oriented evaluation methodology for machine reasoning.

A novel programming paradigm, named ‘Epistemic Programming’, is proposed in order to provide scientists with a computational methodology and computational tools to program their epistemic processes in scientific discovery. The programming paradigm regards conditionals as the subject of computing, takes primary epistemic operations as basic operations of computing, and regards epistemic processes as the subject of programming. Some fundamental observations and assumptions on scientific discovery processes and their automation are presented as the fundamentals of this new research direction. Some research problems in this direction are set up. It is discussed why classical mathematical logic and its various classical conservative extensions cannot satisfactorily underlie epistemic processes. As the logic foundation to underlie epistemic programming, a strong relevant logic model of epistemic processes in scientific discovery is presented, and epistemic programs are defined based on the model.

Modeling epistemic processes in scientific discovery satisfactorily is an indispensable step to automating scientific discovery processes. This paper presents some significant fundamental observations and assumptions on scientific discovery processes and their automation at first. Based on the observations and assumptions, the paper shows why classical mathematical logic, its various classical conservative extensions, and traditional (weak) relevant logics cannot satisfactorily underlie epistemic processes in scientific discovery, and then presents a strong relevant logic model of epistemic processes in scientific discovery. A novel program paradigm, named ‘Epistemic Programming’, based on the strong relevant logic model of epistemic processes is also presented.

Recently, with the application progress of AIGC tools based on large language models (LLMs), led by ChatGPT, many AI experts and more non-professionals are trumpeting the “reasoning ability” of the LLMs. The present author considers that the so-called “reasoning ability” of LLMs are just illusions of those people who with vague concepts. In fact, the LLMs can never have the true reasoning ability. This paper intents to explain that, because the essential limitations of their working principle, the LLMs can never have the ability of true correct reasoning.