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International Center for Computational Logic dumped with wikiteam3 tools.

International Center for Computational Logic dumped with wikiteam3 tools.

International Center for Computational Logic dumped with WikiTeam tools.

We present a calculus providing a Curry-Howard correspondence to classical logic represented in the sequent calculus with explicit structural rules, namely weakening and contraction. These structural rules introduce explicit erasure and duplication of terms, respectively. We present a type system for which we prove the type-preservation under reduction. A mutual relation with classical calculus featuring implicit structural rules has been studied in detail. From this analysis we derive strong normalisation property.

40 p. ; 28 cm

The report describes the research work that is in progress in the Computational Logic Project at Syracuse University. A general introduction to the whole subject area is presented in the opening section, in which an attempt is made to view the field in a broad perspective and to relate it to the concerns of computer science and of artificial intelligence. The sections which follow the introduction are brief accounts of the particular problems which are currently under investigation. These problems are all related, in various ways, to the effort to design computational methods of inference-making for certain formal languages. (Author)

The report describes the research work that is in progress in the Computational Logic Project at Syracuse University. A general introduction to the whole subject area is presented in the opening section, in which an attempt is made to view the field in a broad perspective and to relate it to the concerns of computer science and of artificial intelligence. The sections which follow the introduction are brief accounts of the particular problems which are currently under investigation. These problems are all related, in various ways, to the effort to design computational methods of inference-making for certain formal languages. (Author)

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Introduction : Human Papillomavirus (HPV) is one of the most common sexually transmitted infections (STIs) and responsible for approximately 99% of cervical cancers in the world. Thus, the objective of this work was to develop a computational tool for HPV risk prediction by fuzzy logic. Material and Methods : This involves the development of a computational model using fuzzy logic tools to predict women with a greater predisposition to exposure and infection by HPV. The health, lifestyle, and sexual data of adult women were collected using a semistructured questionnaire, oncotic cytology occurred from the analysis of the vaginal smear, and DNA-HPV research was carried out through the chain reaction of the Nested polymerase (PGMY09/11 (first-round PCR) and GP + 5/GP + 6 (second round PCR) using the Platinum ™ Taq DNA Polymerase system (Invitrogen ™, NY, USA). in the literature and later these were added to the results obtained from the analysis of the participants' data to construct the calculation with the determination of the risk. Results : After the statistical analysis, the RISK set was concatenated into 3 sets (green, yellow, and red), the fuzzified data obtained as variables for availability in the risk calculator the following items: Green = [0-30%], low risk; Yellow = [31-50%], medium risk; RED = above 50%, high risk of HPV infection. os: 400 results of the epidemiological and cervical findings of the women participating in the research were used for training the software and 162 for system validation. After evidenced statistical data, the insertion of the results in the database started. Conclusion : With the results obtained Fuzzy inference system can be as well adopted for the screening for HPV as this will in turn helps to reduce the mortality rate in cases with cancer. This expert system is user-friendly and carries out screening based on patients 'complain (clinical and laboratory data) to a medical expert.

We show that context semantics can be fruitfully applied to the quantitative analysis of proof normalization in linear logic. In particular, context semantics lets us define the weight of a proof-net as a measure of its inherent complexity: it is both an upper bound to normalization time (modulo a polynomial overhead, independently on the reduction strategy) and a lower bound to the number of steps to normal form (for certain reduction strategies). Weights are then exploited in proving strong soundness theorems for various subsystems of linear logic, namely elementary linear logic, soft linear logic and light linear logic.

This article is from Frontiers in Computational Neuroscience , volume 8 . Abstract In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e., dynamic logic-gates (DLGs). The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a non-uniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra.

This article is from Frontiers in Computational Neuroscience , volume 8 . Abstract In 1943 McCulloch and Pitts suggested that the brain is composed of reliable logic-gates similar to the logic at the core of today's computers. This framework had a limited impact on neuroscience, since neurons exhibit far richer dynamics. Here we propose a new experimentally corroborated paradigm in which the truth tables of the brain's logic-gates are time dependent, i.e., dynamic logic-gates (DLGs). The truth tables of the DLGs depend on the history of their activity and the stimulation frequencies of their input neurons. Our experimental results are based on a procedure where conditioned stimulations were enforced on circuits of neurons embedded within a large-scale network of cortical cells in-vitro. We demonstrate that the underlying biological mechanism is the unavoidable increase of neuronal response latencies to ongoing stimulations, which imposes a non-uniform gradual stretching of network delays. The limited experimental results are confirmed and extended by simulations and theoretical arguments based on identical neurons with a fixed increase of the neuronal response latency per evoked spike. We anticipate our results to lead to better understanding of the suitability of this computational paradigm to account for the brain's functionalities and will require the development of new systematic mathematical methods beyond the methods developed for traditional Boolean algebra.

Information Integration is a young and exciting field with enormous research and commercial significance in the new world of the Information Society. It stands at the crossroad of Databases and Artificial Intelligence requiring novel techniques that bring together different methods from these fields. Information from disparate heterogeneous sources often with no a-priori common schema needs to be synthesized in a flexible, transparent and intelligent way in order to respond to the demands of a query thus enabling a more informed decision by the user or application program. The field although relatively young has already found many practical applications particularly for integrating information over the World Wide Web. This paper gives a brief introduction of the field highlighting some of the main current and future research issues and application areas. It attempts to evaluate the current and potential role of Computational Logic in this and suggests some of the problems where logic-based techniques could be used.

Information Integration is a young and exciting field with enormous research and commercial significance in the new world of the Information Society. It stands at the crossroad of Databases and Artificial Intelligence requiring novel techniques that bring together different methods from these fields. Information from disparate heterogeneous sources often with no a-priori common schema needs to be synthesized in a flexible, transparent and intelligent way in order to respond to the demands of a query thus enabling a more informed decision by the user or application program. The field although relatively young has already found many practical applications particularly for integrating information over the World Wide Web. This paper gives a brief introduction of the field highlighting some of the main current and future research issues and application areas. It attempts to evaluate the current and potential role of Computational Logic in this and suggests some of the problems where logic-based techniques could be used.

A simplified version of Montague's intensional logic is presented and explicated in terms of computational analogues. Several examples are presented in which Montague's formalism is coupled with a procedural interpretation. A natural translation is given from intensional logic into LISP. The relevance of this research to composition of meaning, knowledge representation, context and expectation is discussed. (Author)

A simplified version of Montague's intensional logic is presented and explicated in terms of computational analogues. Several examples are presented in which Montague's formalism is coupled with a procedural interpretation. A natural translation is given from intensional logic into LISP. The relevance of this research to composition of meaning, knowledge representation, context and expectation is discussed. (Author)

A theory has been developed for reasoning about what is logically possible. The immediate motivation for this research is that advanced logic programming languages involved a nondeducibility primitive (THNOT is planner, one use of CUT in PROLOG) Which says to infer something if its negation is not deducible. The immediate significance of these results is that we now have effective axiomatizations for proving the correctness of logic programs involving non-deductibility primitives, and of various kinds of truth maintenance systems. However, the overall significance of this result transcends this particular application to program verification, because it provides the correct axiomatization of this concept, and much of the research on the logical representation of knowledge in artificial intelligence presupposes a solution to this very problem.

A theory has been developed for reasoning about what is logically possible. The immediate motivation for this research is that advanced logic programming languages involved a nondeducibility primitive (THNOT is planner, one use of CUT in PROLOG) Which says to infer something if its negation is not deducible. The immediate significance of these results is that we now have effective axiomatizations for proving the correctness of logic programs involving non-deductibility primitives, and of various kinds of truth maintenance systems. However, the overall significance of this result transcends this particular application to program verification, because it provides the correct axiomatization of this concept, and much of the research on the logical representation of knowledge in artificial intelligence presupposes a solution to this very problem.

A theory has been developed for reasoning about what is logically possible. The immediate motivation for this research is that advanced logic programming languages involved a nondeducibility primitive (THNOT is planner, one use of CUT in PROLOG) Which says to infer something if its negation is not deducible. The immediate significance of these results is that we now have effective axiomatizations for proving the correctness of logic programs involving non-deductibility primitives, and of various kinds of truth maintenance systems. However, the overall significance of this result transcends this particular application to program verification, because it provides the correct axiomatization of this concept, and much of the research on the logical representation of knowledge in artificial intelligence presupposes a solution to this very problem.

A theory has been developed for reasoning about what is logically possible. The immediate motivation for this research is that advanced logic programming languages involved a nondeducibility primitive (THNOT is planner, one use of CUT in PROLOG) Which says to infer something if its negation is not deducible. The immediate significance of these results is that we now have effective axiomatizations for proving the correctness of logic programs involving non-deductibility primitives, and of various kinds of truth maintenance systems. However, the overall significance of this result transcends this particular application to program verification, because it provides the correct axiomatization of this concept, and much of the research on the logical representation of knowledge in artificial intelligence presupposes a solution to this very problem.

We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or G{\" o}del number) as input and output the corresponding probability. Similarly, CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps.

We initiate a formal study of logical inferences in context of the measure problem in cosmology or what we call cosmic logic. We describe a simple computational model of cosmic logic suitable for analysis of, for example, discretized cosmological systems. The construction is based on a particular model of computation, developed by Alan Turing, with cosmic observers (CO), cosmic measures (CM) and cosmic symmetries (CS) described by Turing machines. CO machines always start with a blank tape and CM machines take CO's Turing number (also known as description number or G{\" o}del number) as input and output the corresponding probability. Similarly, CS machines take CO's Turing number as input, but output either one if the CO machines are in the same equivalence class or zero otherwise. We argue that CS machines are more fundamental than CM machines and, thus, should be used as building blocks in constructing CM machines. We prove the non-computability of a CS machine which discriminates between two classes of CO machines: mortal that halts in finite time and immortal that runs forever. In context of eternal inflation this result implies that it is impossible to construct CM machines to compute probabilities on the set of all CO machines using cut-off prescriptions. The cut-off measures can still be used if the set is reduced to include only machines which halt after a finite and predetermined number of steps.

Advanced decision analytics, fuzzy logic, artificial intelligence, and computational thinking may be used to improve personnel selection in today's dynamic industry. This research introduces a decision-support framework that uses AI approaches based on fuzzy logic to handle complicated decision-making and improve personnel selection. Hiring the right person for the job is very significant for any business. However, numerous companies have Human Resources (HR) teams that deal with this issue. This study created a staff selection method using the Fuzzy Simple Additive Weighted (FSAW) Method, which considers the applicants' personalities and the fact that people are very subjective. The study aimed to find a method to hire people using fuzzy logic. A three-level plan was made to keep track of the information. Applicants would be ranked by how well they would fit the job. Personality was the most important thing to consider. Someone best does the job with the right skills and natural traits or abilities. Finally, the study showed that the best people can be hired, leading to work success. The results show that this hybrid strategy increases the consistency of decision-making while improving the accuracy, sensitivity and fairness of personnel selection across several case scenarios.

This paper studies an optimal control problem for continuous-time stochastic systems subject to reachability objectives specified in a subclass of metric interval temporal logic specifications, a temporal logic with real-time constraints. We propose a probabilistic method for synthesizing an optimal control policy that maximizes the probability of satisfying a specification based on a discrete approximation of the underlying stochastic system. First, we show that the original problem can be formulated as a stochastic optimal control problem in a state space augmented with finite memory and states of some clock variables. Second, we present a numerical method for computing an optimal policy with which the given specification is satisfied with the maximal probability in point-based semantics in the discrete approximation of the underlying system. We show that the policy obtained in the discrete approximation converges to the optimal one for satisfying the specification in the continuous or dense-time semantics as the discretization becomes finer in both state and time. Finally, we illustrate our approach with a robotic motion planning example.

This paper studies an optimal control problem for continuous-time stochastic systems subject to reachability objectives specified in a subclass of metric interval temporal logic specifications, a temporal logic with real-time constraints. We propose a probabilistic method for synthesizing an optimal control policy that maximizes the probability of satisfying a specification based on a discrete approximation of the underlying stochastic system. First, we show that the original problem can be formulated as a stochastic optimal control problem in a state space augmented with finite memory and states of some clock variables. Second, we present a numerical method for computing an optimal policy with which the given specification is satisfied with the maximal probability in point-based semantics in the discrete approximation of the underlying system. We show that the policy obtained in the discrete approximation converges to the optimal one for satisfying the specification in the continuous or dense-time semantics as the discretization becomes finer in both state and time. Finally, we illustrate our approach with a robotic motion planning example.

A logical theory consists of a language, some axioms schemas, and some rules of inference. However, in developing the proofs of interesting theorems it is often necessary to introduce axioms defining new concepts and operations. Logically speaking, the main results and all of the lemmas along the way are proved in the final theory. To accomodate the practical view of the situation we provide several 'extension principles' by which the user of the theory can add new axioms of a particularly constructive sort. Among these principles is the 'shell principle,' which permits the axiomatization of a 'new' type of inductively constructed object, and the 'definitional principle,' which permits the introduction of an equation defining a recursive function. These extension principles can be considered as rules of inference since they permit one to deduce that certain formulas are theorems. We present the formal syntax of our logic. This syntax is extremely simple and is not the syntax implemented in the theorem-prover. We then develop a large number of syntactic conventions used to describe the axioms and rules of inference. Once we have completed the formal development of the logic we turn, in Section IMPLEMENTEDSYNTAX, to a description of the implemented syntax.

A logical theory consists of a language, some axioms schemas, and some rules of inference. However, in developing the proofs of interesting theorems it is often necessary to introduce axioms defining new concepts and operations. Logically speaking, the main results and all of the lemmas along the way are proved in the final theory. To accomodate the practical view of the situation we provide several 'extension principles' by which the user of the theory can add new axioms of a particularly constructive sort. Among these principles is the 'shell principle,' which permits the axiomatization of a 'new' type of inductively constructed object, and the 'definitional principle,' which permits the introduction of an equation defining a recursive function. These extension principles can be considered as rules of inference since they permit one to deduce that certain formulas are theorems. We present the formal syntax of our logic. This syntax is extremely simple and is not the syntax implemented in the theorem-prover. We then develop a large number of syntactic conventions used to describe the axioms and rules of inference. Once we have completed the formal development of the logic we turn, in Section IMPLEMENTEDSYNTAX, to a description of the implemented syntax.

A logical theory consists of a language, some axioms schemas, and some rules of inference. However, in developing the proofs of interesting theorems it is often necessary to introduce axioms defining new concepts and operations. Logically speaking, the main results and all of the lemmas along the way are proved in the final theory. To accomodate the practical view of the situation we provide several 'extension principles' by which the user of the theory can add new axioms of a particularly constructive sort. Among these principles is the 'shell principle,' which permits the axiomatization of a 'new' type of inductively constructed object, and the 'definitional principle,' which permits the introduction of an equation defining a recursive function. These extension principles can be considered as rules of inference since they permit one to deduce that certain formulas are theorems. We present the formal syntax of our logic. This syntax is extremely simple and is not the syntax implemented in the theorem-prover. We then develop a large number of syntactic conventions used to describe the axioms and rules of inference. Once we have completed the formal development of the logic we turn, in Section IMPLEMENTEDSYNTAX, to a description of the implemented syntax.

Using an algebraic framework we solve a problem posed in [5] and [7] about the axiomatizability of a quantum computational type logic related to fuzzy logic. A Hilbert-style calculus is developed obtaining an algebraic strong completeness theorem.

Following a review of metric, ultrametric and generalized ultrametric, we review their application in data analysis. We show how they allow us to explore both geometry and topology of information, starting with measured data. Some themes are then developed based on the use of metric, ultrametric and generalized ultrametric in logic. In particular we study approximation chains in an ultrametric or generalized ultrametric context. Our aim in this work is to extend the scope of data analysis by facilitating reasoning based on the data analysis; and to show how quantitative and qualitative data analysis can be incorporated into logic programming.

The following topics are covered in viewgraph form: (1) hardware verification; (2) Boyer-Moore logic; (3) core RISC; (4) the FM8502 fabrication, implementation specification, and pinout; (5) hardware description language; (6) arithmetic logic generator; (7) near term expected results; (8) present trends; (9) future directions; (10) collaborations and technology transfer; and (11) technology enablers.

The following topics are covered in viewgraph form: (1) hardware verification; (2) Boyer-Moore logic; (3) core RISC; (4) the FM8502 fabrication, implementation specification, and pinout; (5) hardware description language; (6) arithmetic logic generator; (7) near term expected results; (8) present trends; (9) future directions; (10) collaborations and technology transfer; and (11) technology enablers.

The following topics are covered in viewgraph form: (1) hardware verification; (2) Boyer-Moore logic; (3) core RISC; (4) the FM8502 fabrication, implementation specification, and pinout; (5) hardware description language; (6) arithmetic logic generator; (7) near term expected results; (8) present trends; (9) future directions; (10) collaborations and technology transfer; and (11) technology enablers.

This final report is provided to satisfy a contract deliverable. We summarize here our results in automated reasoning under this contract. The development of the Boyer-Moor 'Nqthm' theorem prover was supported in part for approximately 20 years by the Office of Naval Research. The long range objective of this research has been to enable programmers to produce software that is mathematically proven to meet its specifications by using mechanical theorem- proving programs that check proofs.

The logical framework LF is a type theory defined by Harper, Honsell and Plotkin. It is well-suited to serve as'a meta language to represent deductive systems. LF and its logic programming implementation Elf are also well-suited to represent meta-theoretic proofs and their computational content, but search for such proofs lies outside the framework. This thesis proposes a computational meta logic (MLF) for the Horn fragment of LF. The Horn fragment is a significant restriction of LF but it is powerful enough to represent non-trivial problems. This thesis demonstrates how MLF can be used for the problem of compiler verification. It also discusses some theoretical properties of MLF.

A recursive multisensor association algorithm based on fuzzy logic has been developed. It simultaneously determines fuzzy grades of membership and fuzzy cluster centers. It is capable of associating data from various sensor types. In its simplest form, it makes no assumption about noise statistics as many association algorithms do. The algorithm is capable of performing without operator intervention. It associates data from the same target for multiple sensor types. The algorithm also provides an estimate of the number of targets present, reduced noise estimates of the quantities being measured, and a measure of confidence to assign to the data association. A comparison of the algorithm to a more conventional Bayesian association algorithm is provided. Data from both the electronic support measures (ESM) and radar systems are noisy, and ESM data are also intermittent. The data has probability of detection less than unity. The effect of a large number of targets being present in the data on parameter estimation, determination of the number of targets, and multisensor data association is examined. Finally, issues related to computational complexity are discussed.

A recursive multisensor association algorithm based on fuzzy logic has been developed. It simultaneously determines fuzzy grades of membership and fuzzy cluster centers. It is capable of associating data from various sensor types. In its simplest form, it makes no assumption about noise statistics as many association algorithms do. The algorithm is capable of performing without operator intervention. It associates data from the same target for multiple sensor types. The algorithm also provides an estimate of the number of targets present, reduced noise estimates of the quantities being measured, and a measure of confidence to assign to the data association. A comparison of the algorithm to a more conventional Bayesian association algorithm is provided. Data from both the electronic support measures (ESM) and radar systems are noisy, and ESM data are also intermittent. The data has probability of detection less than unity. The effect of a large number of targets being present in the data on parameter estimation, determination of the number of targets, and multisensor data association is examined. Finally, issues related to computational complexity are discussed.