本文選題：回答集程序 + 智能空間�。� 參考：《北京工業(yè)大學(xué)》2016年碩士論文

【摘要】：信息技術(shù)的快速發(fā)展使計算機軟件的執(zhí)行效率和硬件的性能都得到了很大的提升。在當(dāng)今時代,很多設(shè)備都具有計算能力與數(shù)字通信能力,而且這些設(shè)備之間可以互相交換信息和使用對方提供的功能。智能空間作為一個融合了計算、信息設(shè)備和多模態(tài)傳感器的工作空間,能夠?qū)崿F(xiàn)設(shè)備間的自發(fā)交互,但其中的設(shè)備、運營商和產(chǎn)品領(lǐng)域種類多樣,實現(xiàn)自發(fā)交互需要各個參與者之間建立一個共同的標(biāo)準。因此,相關(guān)研究學(xué)者研發(fā)了Smart-M3平臺用于實現(xiàn)智能空間中的自發(fā)交互操作,完成設(shè)備間信息和服務(wù)的共享與存取。在此基礎(chǔ)上,Vesa Luukkala等學(xué)者為了提高空間的推理能力,將回答集程序ASP與Smart-M3平臺進行了整合,采用固定優(yōu)先關(guān)系的方法解決了智能空間中的服務(wù)決策問題。但是沒有考慮用戶上下文信息與環(huán)境信息兩個影響因素,導(dǎo)致在服務(wù)推薦時往往不能滿足用戶的真實需求。針對這種問題,本文對智能空間中的上下文感知問題進行了比較深入的研究,開展了以下兩個方面的工作:(1)針對現(xiàn)階段采用固定優(yōu)先關(guān)系的方法解決空間中服務(wù)決策問題的局限性,本文提出了一種基于回答集程序的智能空間中上下文感知框架,旨在提高空間的動態(tài)推理能力。該框架首先建立了一種通用的上下文本體模型,并設(shè)計了上下文推理結(jié)構(gòu);然后定義了上下文推理規(guī)則,在規(guī)則中利用缺省決策理論動態(tài)決策上下文服務(wù)的優(yōu)先關(guān)系,與空間中的上下文信息一起組成回答集程序,該程序的回答集對應(yīng)的是當(dāng)前上下文信息動態(tài)推理的結(jié)果。最后,通過引入一個應(yīng)用實例說明了該框架在具體場景中能有效地完成上下文動態(tài)推理,實現(xiàn)了智能空間中具有上下文感知的動態(tài)服務(wù)決策。(2)進一步的,由于上下文推理過程中使用的原始信息大多來源于空間的物理設(shè)備,不可避免的存在信息更新不及時或信息丟失等問題,導(dǎo)致上下文信息出現(xiàn)不一致的情況,從而使推理過程無法進行。目前大多數(shù)的上下文不一致檢測方法存在著建模復(fù)雜的缺點。為了避免這種問題,本文提出了基于元程序的方法檢測上下文信息中存在的不一致。首先,使用元程序構(gòu)建上下文不一致的檢測程序,然后使用回答集求解器Smodels求解該程序的回答集,實現(xiàn)上下文信息不一致的自動檢測;最后,對檢測到的不一致信息執(zhí)行消除操作策略。這樣便解決了上下文信息的不一致性,從而保證上下文推理算法的正確執(zhí)行。
[Abstract]:With the rapid development of information technology, the execution efficiency and hardware performance of computer software have been greatly improved. In modern times, many devices have computing power and digital communication ability, and these devices can exchange information and use the function provided by the other side. As a workspace that combines computing, information equipment and multimodal sensors, intelligent space can realize spontaneous interaction between devices, but the types of devices, operators and products are diverse. The realization of spontaneous interaction requires the establishment of a common standard among the participants. Therefore, related researchers have developed a Smart-M3 platform to realize spontaneous interaction in intelligent space, and to share and access information and services between devices. On this basis, in order to improve the spatial reasoning ability of Vesa Luukkala and other scholars, the answer set program ASP is integrated with the Smart-M3 platform, and the service decision problem in the intelligent space is solved by the method of fixed priority relation. However, the user context information and environment information are not taken into account, which leads to the failure to meet the real needs of users in service recommendation. In order to solve this problem, the context-aware problem in intelligent space is deeply studied in this paper. This paper presents a context-aware framework in intelligent space based on answer set program, aiming at the limitation of using fixed priority relation to solve the problem of service decision in space. The aim is to improve the spatial dynamic reasoning ability. The framework first establishes a general context ontology model and designs a contextual reasoning structure, then defines the contextual reasoning rules and uses default decision theory in the rules to dynamically determine the precedence of context services. The answer set is composed with the context information in the space. The answer set of the program corresponds to the result of the dynamic inference of the current context information. Finally, an application example is introduced to illustrate that the framework can effectively accomplish contextual dynamic reasoning in specific scenarios, and realize dynamic service decision with context-aware in intelligent space. Because most of the original information used in the process of contextual reasoning comes from the physical equipment of the space, there are inevitable problems such as the information updating is not timely or the information is lost, which leads to the inconsistency of the context information. As a result, the reasoning process can not be carried out. At present, most of the context inconsistent detection methods have the disadvantage of complex modeling. In order to avoid this problem, this paper proposes a meta-program-based approach to detect inconsistencies in context information. First, the meta program is used to construct the context inconsistent detection program, and then the answer set solver Smodels is used to solve the answer set of the program to realize the automatic detection of the inconsistency of the context information. Perform an action elimination strategy for detected inconsistencies. In this way, the inconsistency of context information is solved, and the correct execution of contextual reasoning algorithm is ensured.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.1

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