An Efficient Rule-Based Distributed Reasoning Framework for Resource-bounded Systems

Rakib, Abdur; Uddin, Ijaz

doi:10.1007/s11036-018-1140-x

Cited by 12 publications

(5 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, we must establish algorithms/standards to regulate this situation. The [8] present study has further refined the previous models of context-aware systems [8], which were developed for resource-constrained devices using rule-based systems based on the logical framework [9]. The improvement in this research lies in developing new calculation methods for memory allocation.…”

Section: Introductionmentioning

confidence: 76%

“…In the early works in Pervasive and Ubiquitous Computing, context awareness is referred to as the location of the objects and/or people [11]. In recent years, the context has been extended to other factors, such as the social and physical aspects of an entity and the activities of the user [8], [12], [13]. Similarly, [11], [14]- [19] evaluated the user's identity and social situation, as well as the user's location, environmental and/or temporal information, the objects and people in their immediate surroundings, as well as any changes to those objects.…”

Section: Related Workmentioning

confidence: 99%

“…This section will enhance the clarity and comprehensibility of the results. In this table, the Rule Base (RB) column shows the total number of rules per rule set, and we consider it equivalent to RETE algorithm-based systems because the RETE algorithm loads all the rules in memory and compares them one by one during processing [8].…”

Section: A Time and Space Requirementmentioning

confidence: 99%

See 2 more Smart Citations

A Resource Aware Memory Requirement Calculation Model for Memory Constrained Context-Aware Systems

Ali,

Arshad,

Uddin

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Smart spaces are physical environments equipped with sensors, actuators, and other computing devices to gather data and provide intelligent services to users. These spaces are made possible by ubiquitous computing, particularly context-aware computing. Although these systems are mainly implemented on mobile and other resource-constrained wearable devices, different techniques have been adopted for their implementation. Rule-based reasoning is a relatively easyto-implement approach that can solve real-world problems. Rule-based systems rely on a set of assertions that constitute the working memory and a set of rules that govern what should be done with the set of assertions. Despite its relative simplicity, the working memory size is a critical factor in developing these systems, particularly for resource-constrained devices. In this paper, we propose techniques for efficiently calculating the working memory size. Our results show that all three techniques, DWM, APS, and SAPS, performed well in different ways. However, APS and SAPS consumed from 25% to 100% less memory than existing techniques.

show abstract

Section: Introductionmentioning

confidence: 76%

Section: Related Workmentioning

confidence: 99%

Section: A Time and Space Requirementmentioning

confidence: 99%

See 1 more Smart Citation

A Resource Aware Memory Requirement Calculation Model for Memory Constrained Context-Aware Systems

Ali,

Arshad,

Uddin

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although, the development of context-aware applications for reasoning on resource-bounded mobile devices is challenging. Rakib & Uddin (2019) presented a context-aware framework with a lightweight rule engine and a wide range of user preferences to decrease the number of rules while inferring personalized contexts. The authors confirmed that associated rules can be reduced in order to enhance the inference engine efficiency in terms of accuracy, execution speed, total execution time, and execution cost.…”

Section: Background and Related Workmentioning

confidence: 99%

Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Rahmani

Azhir

Ali

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

show abstract

“…Distributed reasoning is a central aspect required by many applications proposed in the last decades, including mobile and ubiquitous systems, smart spaces and Ambient Intelligence systems [1][2][3] -and more recently the Internet of Things (IoT) [4,5] -and the Semantic Web [6]. In these types of systems, distributed entities, called agents, are able to capture or receive information from their environment and from other agents, and are often able to derive new knowledge or making decisions based on the available knowledge.…”

Section: Introductionmentioning

confidence: 99%

Argumentation-based multi-agent distributed reasoning in dynamic and open environments

Monte-Alto,

Morveli-Espinoza,

Tacla

2024

Knowl Inf Syst

View full text Add to dashboard Cite

This work presents an approach for distributed and contextualized reasoning in multi-agent systems, considering environments in which agents may have incomplete, uncertain and inconsistent knowledge. Knowledge is represented by defeasible logic with mapping rules, which model the capability of agents to acquire knowledge from other agents during reasoning. Based on such knowledge representation, it is proposed an argumentation-based reasoning model that enables distributed building of reusable argument structures to support conclusions. Conflicts between arguments are resolved by an argument strength calculation that considers the trust among agents and the degree of similarity between knowledge of different agents, based on the intuition that greater similarity between knowledge defined by different agents implies in less uncertainty about the validity of the built argument. Contextualized reasoning is supported through sharing of relevant knowledge by an agent when issuing queries to other agents, which enable the cooperating agents to be aware of knowledge not known a priori but that is important to reach a reasonable conclusion given the context of the agent that issued the query. A distributed algorithm is presented and analytically and experimentally evaluated asserting its computational feasibility. Finally, our approach is compared to related work, highlighting the contributions presented, demonstrating its applicability in a broader range of scenarios, and presenting perspectives for future work.

show abstract

An Efficient Rule-Based Distributed Reasoning Framework for Resource-bounded Systems

Cited by 12 publications

References 36 publications

A Resource Aware Memory Requirement Calculation Model for Memory Constrained Context-Aware Systems

A Resource Aware Memory Requirement Calculation Model for Memory Constrained Context-Aware Systems

Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Argumentation-based multi-agent distributed reasoning in dynamic and open environments

Contact Info

Product

Resources

About