Creation of intelligent agents (IAs) became a basic trend in artificial intelligence in recent years. One of the most important requirements to such systems is real-time decision making when operating in open dynamic and uncertain environments. The efficiency of IAs in hard real-time applications can be improved by hardware support. Rule-based (production) knowledge are widely used in different subsystems of IAs to represent both domain and control knowledge.In this paper a known approaches to design and implementation of inference processor for rule-based systems are considered and a novel one is presented. An approach is based on the concept of universal interpreter for rule-based systems and its implementation on microprogram level. The design tasks and processor's architecture based on Rete-network with multiple input βnodes are considered. A design methodology for such class of devices and corresponding software tool for design process support are described. An implementation of processor using Altera programmable logic devices (PLD) and performance estimation results are presented in conclusion.
An ontology-driven approach to software design and development of situation assessment systems (SAS) for IoT applications is considered. As SAS is used to build the situational model for the external environment, it highly depends on the operational domain. To simplify the transition from the domain description to SAS dataflow process the ontology-driven approach is proposed. The main idea of the approach is to explicitly formalize SAS dataflow process in an ontological form. For this purpose, a domain-independent SAS ontology is proposed that allows automation of the dataflow process design. The dataflow process ontology is used to automate development and runtime stages of SAS lifecycle. The proposed ontology is included into the proposed instrument set. The set can be used to build SAS systems for different domains described with OWL ontology. The set is evaluated on a traffic control scenario.
Evolution of cyber-physical systems (CPS) and extension of their application areas complicate, among other things, their software design and development. This requires improvements in programming techniques used to build CPS. One of the important tasks arising in complex CPS is a situation assessment (SA) based on data received from diverse sources. In the chapter, an ontology-driven approach for CPS SA software design and development automation is proposed. The approach is based on the JDL data fusion model and flexible enough to be applied for any class of CPS applications. In contrast to known approaches, ontologies are used not only for domain knowledge representation but also for SA calculation process formalization. It provides a higher level of automation of SA software synthesis and, in the end, increases design and development efficiency.
Ɉɧɬɨɥɨɝɢɹ ɩɪɨɟɤɬɢɪɨɜɚɧɢɹ ɬɨɦ ʋ3(21) ȺɧɧɨɬɚɰɢɹɊɚɫɫɦɚɬɪɢɜɚɟɬɫɹ ɨɧɬɨɥɨɝɢɱɟɫɤɢɣ ɩɨɞɯɨɞ ɤ ɩɪɨɟɤɬɢɪɨɜɚɧɢɸ ɩɨɞɫɢɫɬɟɦ ɨɰɟɧɤɢ ɨɛɫɬɚɧɨɜɤɢ (ɉɈɈ) ɢɧɬɟɥɥɟɤɬɭɚɥɶɧɵɯ ɚɝɟɧɬɨɜ (ɂȺ), ɮɭɧɤɰɢɨɧɢɪɭɸɳɢɯ ɜ ɞɢɧɚɦɢɱɟɫɤɢɯ ɦɧɨɝɨɚɝɟɧɬɧɵɯ ɫɪɟɞɚɯ, ɜ ɬɨɦ ɱɢɫɥɟ ɜ ɭɫɥɨɜɢɹɯ ɝɪɭɩɩɨɜɨɝɨ ɩɪɨɬɢɜɨɞɟɣɫɬɜɢɹ. ɉɨɞɯɨɞ ɨɫɧɨɜɚɧ ɧɚ ɜɵɞɟɥɟɧɢɢ ɚɫɩɟɤɬɨɜ ɩɨɫɬɪɨɟ-ɧɢɹ ɉɈɈ, ɢɧɜɚɪɢɚɧɬɧɵɯ ɤɨɧɤɪɟɬɧɵɦ ɨɛɥɚɫɬɹɦ ɩɪɢɦɟɧɟɧɢɹ ɚɝɟɧɬɨɜ ɢ ɪɟɲɚɟɦɵɦ ɢɦɢ ɡɚɞɚɱɚɦ. ȼ ɤɚɱɟɫɬɜɟ ɬɟɨɪɟɬɢɱɟɫɤɨɝɨ ɛɚɡɢɫɚ ɩɪɨɰɟɫɫɚ ɩɪɨɟɤɬɢɪɨɜɚɧɢɹ ɩɪɟɞɥɨɠɟɧɨ ɫɟɦɟɣɫɬɜɨ ɦɨɞɟɥɟɣ, ɮɨɪɦɚ-ɥɢɡɭɸɳɢɯ ɪɚɡɥɢɱɧɵɟ ɚɫɩɟɤɬɵ ɩɨɫɬɪɨɟɧɢɹ ɢ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɉɈɈ, ɫ ɭɱɺɬɨɦ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɪɚɡɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ ɜɧɭɬɪɟɧɧɟɣ ɦɨɞɟɥɢ ɦɢɪɚ ɚɝɟɧɬɚ, ɨɫɨɛɟɧɧɨɫɬɟɣ ɨɪɝɚɧɢɡɚɰɢɢ ɩɪɨɰɟɫɫɚ ɜɵɱɢɫ-ɥɟɧɢɣ ɢ ɷɬɚɩɨɜ ɩɪɨɟɤɬɢɪɨɜɚɧɢɹ ɉɈɈ. ɉɪɟɞɥɨɠɟɧɧɵɟ ɦɨɞɟɥɢ ɩɨɡɜɨɥɹɸɬ ɫ ɟɞɢɧɵɯ ɩɨɡɢɰɢɣ ɩɪɨɟɤ-ɬɢɪɨɜɚɬɶ ɉɈɈ ɞɥɹ ɪɚɡɧɵɯ ɂȺ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɨɧɬɨɥɨɝɢɣ. ȼɵɞɟɥɟɧɵ ɞɜɚ ɤɥɚɫɫɚ ɨɧɬɨɥɨɝɢɣ, ɩɨɞ-ɞɟɪɠɢɜɚɸɳɢɯ ɩɪɨɰɟɫɫ ɩɪɨɟɤɬɢɪɨɜɚɧɢɹ: ɨɧɬɨɥɨɝɢɹ ɉɈɈ ɢ ɨɧɬɨɥɨɝɢɢ ɤɨɧɤɪɟɬɧɵɯ ɩɪɟɞɦɟɬɧɵɯ ɨɛɥɚɫɬɟɣ. Ɉɧɬɨɥɨɝɢɹ ɉɈɈ ɨɛɟɫɩɟɱɢɜɚɟɬ ɢɧɬɟɝɪɚɰɢɸ ɜ ɢɧɜɚɪɢɚɧɬɧɵɣ ɤɚɪɤɚɫ ɩɪɨɝɪɚɦɦɧɨɝɨ ɤɨɞɚ, ɪɟɚɥɢɡɭɸɳɟɝɨ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɨɛɫɬɚɧɨɜɤɢ ɞɥɹ ɤɨɧɤɪɟɬɧɨɝɨ ɚɝɟɧɬɚ. Ɉɧɬɨɥɨɝɢɹ ɩɪɟɞɦɟɬɧɨɣ ɨɛɥɚ-ɫɬɢ ɪɚɫɲɢɪɹɟɬ ɨɧɬɨɥɨɝɢɸ ɉɈɈ ɢ ɩɨɞɞɟɪɠɢɜɚɟɬ ɚɜɬɨɦɚɬɢɱɟɫɤɭɸ ɝɟɧɟɪɚɰɢɸ ɩɪɨɝɪɚɦɦɧɨɝɨ ɤɨɞɚ ɢ ɟɝɨ ɢɧɬɟɝɪɚɰɢɸ ɜ ɢɧɜɚɪɢɚɧɬɧɵɣ ɤɚɪɤɚɫ ɉɈɈ. Ƚɟɧɟɪɚɰɢɹ ɩɪɨɝɪɚɦɦɧɨɝɨ ɤɨɞɚ ɉɈɈ ɞɥɹ ɤɨɧɤɪɟɬɧɨ-ɝɨ ɂȺ ɨɛɟɫɩɟɱɢɜɚɟɬɫɹ ɩɪɟɞɥɨɠɟɧɧɵɦɢ ɨɬɨɛɪɚɠɟɧɢɹɦɢ ɨɧɬɨɥɨɝɢɱɟɫɤɢɯ ɤɥɚɫɫɨɜ ɢ ɫɜɨɣɫɬɜ ɜ ɩɪɨ-ɝɪɚɦɦɧɵɟ ɫɬɪɭɤɬɭɪɵ. Ɋɚɫɫɦɨɬɪɟɧ ɩɪɨɬɨɬɢɩ ɢɧɫɬɪɭɦɟɧɬɚɥɶɧɨɣ ɩɥɚɬɮɨɪɦɵ ɪɚɡɪɚɛɨɬɤɢ ɉɈɈ, ɪɟɚ-ɥɢɡɨɜɚɧɧɵɣ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɹɡɵɤɨɜ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɢ ɨɛɪɚɛɨɬɤɢ ɨɧɬɨɥɨɝɢɣ OWL, SPARQL, SPIN ɢ ɹɡɵɤɚ ɩɪɨɝɪɚɦɦɢɪɨɜɚɧɢɹ Java. ɉɨɞɯɨɞ ɢɥɥɸɫɬɪɢɪɭɟɬɫɹ ɩɨɫɬɪɨɟɧɢɟɦ ɉɈɈ ɚɝɟɧɬɚ, ɮɭɧɤɰɢɨɧɢ-ɪɭɸɳɟɝɨ ɜ ɫɪɟɞɟ ɜɢɪɬɭɚɥɶɧɨɝɨ ɮɭɬɛɨɥɚ. ȼɜɟɞɟɧɢɟ ȼ ɩɨɫɥɟɞɧɢɟ ɝɨɞɵ ɨɧɬɨɥɨɝɢɢ ɜɫɟ ɲɢɪɟ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɪɢ ɩɪɨɟɤɬɢɪɨɜɚɧɢɢ ɪɚɡɥɢɱɧɵɯ ɤɥɚɫɫɨɜ ɬɟɯɧɢɱɟɫɤɢɯ ɫɢɫɬɟɦ ɜ ɤɚɱɟɫɬɜɟ ɮɨɪɦɚɥɢɡɨɜɚɧɧɵɯ ɦɨɞɟɥɟɣ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɤɨɧɰɟɩɬɭ-ɚɥɶɧɵɯ ɡɧɚɧɢɣ ɨɛ ɷɬɢɯ ɫɢɫɬɟɦɚɯ [1][2][3]. Ɉɞɧɢɦ ɢɡ ɧɚɢɛɨɥɟɟ ɞɢɧɚɦɢɱɧɨ ɪɚɡɜɢɜɚɸɳɢɯɫɹ ɤɥɚɫ-ɫɨɜ ɫɢɫɬɟɦ ɜ ɨɛɥɚɫɬɢ ɢɫɤɭɫɫɬɜɟɧɧɨɝɨ ɢɧɬɟɥɥɟɤɬɚ ɹɜɥɹɸɬɫɹ ɜ ɧɚɫɬɨɹɳɟɟ ɜɪɟɦɹ ɢɧɬɟɥɥɟɤɬɭ-ɚɥɶɧɵɟ ɚɝɟɧɬɵ (ɂȺ) ɢ ɨɫɧɨɜɚɧɧɵɟ ɧɚ ɧɢɯ ɦɧɨɝɨɚɝɟɧɬɧɵɟ ɫɢɫɬɟɦɵ (ɆȺɋ) [4, 5]. ɂɫɩɨɥɶɡɨɜɚ-ɧɢɸ ɨɧɬɨɥɨɝɢɣ ɩɪɢ ɩɪɨɟɤɬɢɪɨɜɚɧɢɢ ɚɝɟɧɬɧɵɯ ɫɢɫɬɟɦ ɪɚɡɥɢɱɧɨɝɨ ɧɚɡɧɚɱɟɧɢɹ ɩɨɫɜɹɳɟɧɨ ɞɨ-ɫɬɚɬɨɱɧɨ ɦɧɨɝɨ ɢɫɫɥɟɞɨɜɚɧɢɣ, ɜ ɱɚɫɬɧɨɫɬɢ [6, 7]. Ɉɞɧɚɤɨ, ɜɫɥɟɞɫɬɜɢɟ ɲɢɪɨɤɨɝɨ ɪɚɡɧɨɨɛɪɚɡɢɹ ɢ ɫɥɨɠɧɨɫɬɢ ɬɚɤɢɯ ɫɢɫɬɟɦ, ɩɪɨɛɥɟɦɚ ɢɯ ɨɧɬɨɥɨɝɢɱɟɫɤɨɝɨ ɩɪɨɟɤɬɢɪɨɜɚɧɢɹ ɟɳɺ ɞɚɥɟɤɚ ɨɬ ɨɤɨɧɱɚɬɟɥɶɧɨɝɨ ɪɟɲɟɧɢɹ ɢ ɜɟɫɶɦɚ ɚɤɬɭɚɥɶɧɚ ɜ ɧɚɫɬɨɹɳɟɟ ɜɪɟɦɹ.
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