An Inference-Rule-Based Decision Procedure for Verification of Heap-Manipulating Programs with Mutable Data and Cyclic Data Structures

2007 ◽  
pp. 106-121 ◽  
Author(s):  
Zvonimir Rakamarić ◽  
Jesse Bingham ◽  
Alan J. Hu
Keyword(s):  
Data Structures ◽  
Decision Procedure ◽  
Inference Rule ◽  
Rule Based ◽  
Cyclic Data

SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS

2008 ◽  
Vol 16 (02) ◽  
pp. 245-274 ◽  
Author(s):  
BYOUNG-JUN PARK ◽  
WITOLD PEDRYCZ ◽  
SUNG-KWUN OH
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Least Square Method ◽  
Fuzzy Neural Networks ◽  
Least Square ◽  
Rule Based ◽  
Fuzzy Partition ◽  
Fuzzy Neural ◽  
Input Variables

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

Rule based fuzzy cognitive maps and natural language processing in machine ethics

2016 ◽  
Vol 14 (3) ◽  
pp. 231-253 ◽  
Author(s):  
Rollin M. Omari ◽  
Masoud Mohammadian
Keyword(s):  
Decision Making ◽  
Ethical Theory ◽  
Decision Procedure ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Cognitive Maps ◽  
Ethical Principles ◽  
Machine Ethics ◽  
Rule Based ◽  
Content Type

Purpose The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA). Design/methodology/approach The decision results derived by the AMA are acquired via fuzzy logic interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM). Findings Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge. Research limitations/implications This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles. Practical implications To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties. Originality/value This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics.

scholarly journals A last-in first-out stack data structure implemented in DNA

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Annunziata Lopiccolo ◽  
Ben Shirt-Ediss ◽  
Emanuela Torelli ◽  
Abimbola Feyisara Adedeji Olulana ◽  
Matteo Castronovo ◽  
...  
Keyword(s):  
Data Structure ◽  
Nucleic Acid ◽  
Data Structures ◽  
Memory Systems ◽  
Chemical Data ◽  
Successive Reaction ◽  
Open Problems ◽  
Rule Based ◽  
History Of

AbstractDNA-based memory systems are being reported with increasing frequency. However, dynamic DNA data structures able to store and recall information in an ordered way, and able to be interfaced with external nucleic acid computing circuits, have so far received little attention. Here we present an in vitro implementation of a stack data structure using DNA polymers. The stack is able to record combinations of two different DNA signals, release the signals into solution in reverse order, and then re-record. We explore the accuracy limits of the stack data structure through a stochastic rule-based model of the underlying polymerisation chemistry. We derive how the performance of the stack increases with the efficiency of washing steps between successive reaction stages, and report how stack performance depends on the history of stack operations under inefficient washing. Finally, we discuss refinements to improve molecular synchronisation and future open problems in implementing an autonomous chemical data structure.

AGE-P

2010 ◽  
pp. 182-203
Author(s):  
Ralf Salomon ◽  
Stefan Goldmann
Keyword(s):  
Evolutionary Algorithms ◽  
System Dynamics ◽  
Case Studies ◽  
Data Structures ◽  
Real World ◽  
Rule Based ◽  
Non Invasive ◽  
Intelligent Devices ◽  
Smart Appliance ◽  
Traditional Approaches

Smart-appliance ensembles consist of intelligent devices that interact with each other and that are supposed to support their users in an autonomous, non-invasive way. Since both the number and the composition of the participating devices may spontaneously change at any time without any notice, traditional approaches, such as rule-based systems and evolutionary algorithms, are not appropriate mechanisms for their self-organization. Therefore, this chapter describes a new evolutionary framework, called appliancesgo- evolution platform (AGE-P) that accounts for the inherent system dynamics by distributing all data structures and all operations across all participating devices. This chapter illustrates the behavior of this framework by presenting several results obtained from simulations as well as real-world case studies.

XML weak functional dependency and inference rule based on XML Schema

2010 ◽  
Vol 30 (9) ◽  
pp. 2314-2316
Author(s):  
Li-feng YIN ◽  
Hong TIAN
Keyword(s):  
Inference Rule ◽  
Xml Schema ◽  
Functional Dependency ◽  
Rule Based
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