Online dispute resolution: an artificial intelligence perspective

Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.     

基于上下文的知识表示和推理--人工智能的观点

本文中,我们从知识表示和推理(KRR)的角度概括地阐述了上下文推理的概念和基本原理。首先阐述了上下文的概念;然后介绍了上下文推理的三种基本形式和上下文理论的两个基本原理,也就是局部性原理和一致性原理;接着讨论了上下文推理的形式化问题;最后通过对一个叫“魔术盒问题”的求解来展示如何利用多上下文系统MCS对问题进行形式化表示和求解。     

The implementation of artificial intelligence in organizations: A systematic literature review

This study seeks to thoroughly understand the organizational context in which Artificial Intelligence (AI) would be implemented, through a systematic review and analysis of articles published (up to 2021) in 31 journals on information systems, business, management, and operations management. Seventy themes are identified from the literature and categorized into organizational, information systems, technological, and people dimensions for the antecedents, challenges, guidelines, and consequences of AI implementation in organizations. A conceptual framework for understanding AI implementation in organizations is also proposed. This study provides a research agenda to guide future research and facilitate knowledge accumulation and creation on AI implementation.     

Managing the risks of artificial general intelligence: A human factors and ergonomics perspective

Artificial General Intelligence (AGI) is the next and forthcoming evolution of Artificial Intelligence (AI). Though there could be significant benefits to society, there are also concerns that AGI could pose an existential threat. The critical role of Human Factors and Ergonomics (HFE) in the design of safe, ethical, and usable AGI has been emphasized; however, there is little evidence to suggest that HFE is currently influencing development programs. Further, given the broad spectrum of HFE application areas, it is not clear what activities are required to fulfill this role. This article presents the perspectives of 10 researchers working in AI safety on the potential risks associated with AGI, the HFE concepts that require consideration during AGI design, and the activities required for HFE to fulfill its critical role in what could be humanity's final invention. Though a diverse set of perspectives is presented, there is broad agreement that AGI potentially poses an existential threat, and that many HFE concepts should be considered during AGI design and operation. A range of critical activities are proposed, including collaboration with AGI developers, dissemination of HFE work in other relevant disciplines, the embedment of HFE throughout the AGI lifecycle, and the application of systems HFE methods to help identify and manage risks.     

Product creation: An appropriate coupling of human and artificial intelligence

Small batch manufacture dominates the manufacturing sector of a growing number of industrialised countries. The organisational structures and management methods currently adopted in such enterprises are firmly based upon historical developments which started with individual craftsmen. These structures and methods are primarily concerned with the co-ordination of human activities, rather than with the management of theknowledge process underlying the creation of products.This paper argues that it is the failure to understand this knowledge process and its effective integration at aKnowledge Level which presents the real barrier to increased flexibility, not, as is presently perceived, a lack of suitableInformation Level integration. Potential techniques and methodologies for achievingKnowledge Level integration are beginning to emerge from Artificial Intelligence research. Realisation of full Knowledge Level integration will not only require further research into the AI techniques and methodologies involved, but also an understanding of the wider human aspects of their application. Some questions concerning the effective coupling of human and artificial intelligence to achieve Knowledge Level integration of the product creation process are presented.     

A systematic literature review on hardware implementation of artificial intelligence algorithms

Artificial intelligence (AI) and machine learning (ML) tools play a significant role in the recent evolution of smart systems. AI solutions are pushing towards a significant shift in many fields such as healthcare, autonomous airplanes and vehicles, security, marketing customer profiling and other diverse areas. One of the main challenges hindering the AI potential is the demand for high-performance computation resources. Recently, hardware accelerators are developed in order to provide the needed computational power for the AI and ML tools. In the literature, hardware accelerators are built using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks. These accelerators provide high-performance hardware while preserving the required accuracy. In this work, we present a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools. More than 169 different research papers published between the years 2009 and 2019 are studied and analysed.

     

An artificial intelligence approach to language instruction

This paper describes an implemented, prototype system for a sophisticated, intelligent tutor for instruction in a foreign language. The system is an application of artificial intelligence research in natural language, but it implements several ideas that depart from standard approaches to natural language understanding.

For instance, the semantic analyzer diagnoses several kinds of comprehension problems and semantic errors that a student might make. Some fine distinctions in meaning are represented to detect misuse of words. Not only is a model of good syntax included in the tutor, but also a model of incorrect forms, rich enough to pinpoint specific syntactic mistakes. Finding the intended interpretation is complicated by the likelihood of student errors. Therefore, perfect syntactic form is not necessary for semantic analysis of the student's input.

The problems discussed and solutions presented are closely related to the more general problem of how to respond to a natural language input that surpasses the computer's model of language or of context.     

EXSHOF: An artificial intelligence approach to high-order filter synthesis

A two-tier expert-system-based synthesis of high-order filters is developed. Expert-system techniques have been used for choosing a particular biquad structure and for selection of the high-order filter structure as well. The data inputs will be either filter specifications or transfer functions and the output will be a detailed filter circuit with all its element values. EXSHOF ( pert System based ynthesis of igh rder ilter) is totally menu-driven and highly interactive. It assumes little knowledge of computers or filters from the user. The complete package is implemented in Turbo PASCAL and Turbo PROLOG languages.     

多媒体智能：当多媒体遇到人工智能

过去10年中涌现出大量新兴的多媒体应用和服务,带来了很多可以用于多媒体前沿研究的多媒体数据。多媒体研究在图像/视频内容分析、多媒体搜索和推荐、流媒体服务和多媒体内容分发等方向均取得了重要进展。与此同时,由于在深度学习领域所取得的重大突破,人工智能(artificial intelligence,AI)在20世纪50年代被正式视为一门学科之后,迎来了一次“新”的发展浪潮。因此,一个问题就自然而然地出现了：当多媒体遇到人工智能时会带来什么？为了回答这个问题,本文通过研究多媒体和人工智能之间的相互影响引入了多媒体智能的概念。从两个方面探讨多媒体与人工智能之间的相互影响：一是多媒体促使人工智能向着更具可解释性的方向发展;二是人工智能反过来为多媒体研究注入了新的思维方式。这两个方面形成了一个良性循环,多媒体和人工智能在其中不断促进彼此发展。本文对相关研究及进展进行了讨论,并围绕值得进一步探索的研究方向分享见解。希望可以对多媒体智能的未来发展带来新的研究思路。     

An application of artificial intelligence to medical robotics

In this paper an application of Artificial Intelligence (AI) to Medical Robotics is described. Namely, a specific AI technique is employed to generate a sequence of operations understandable by the control system of a robot which is to perform a semi-automatic surgical task. According to this technique, a planner is implemented to translate the “natural” language of the surgeon into the robotic sequence that should be executed by the robot. A robotic simulator has been implemented in order to test the planned sequence in a virtual environment. The planned sequence is then to be input to the medical robotic system, which will execute the surgical operation. The work described in this paper features a high level of originality, since no similar applications of AI to medical robotics could be found in the scientific literature.     

An artificial intelligence approach to speech recognition and understanding

This paper presents the I.R.S.T. Automatic Speech Recognition and Understanding (A.S.R.U.) Research Program for continuously spoken Italian without previous knowledge of the identity of the speaker. The acoustic analysis is performed in time domain and works in real-time. Acoustic ambiguities are overcome by using various levels of contextual information (orthophonic, syntactic, semantic) to formulate hypotheses to be verified by means of an hypothesize and test paradigm. The architecture is an analysis by synthesis loop.     

An artificial intelligence approach for medical picture analysis

This paper reports a first experiment to apply the expert system techniques to image understanding. The expertise comes from two sides: the picture processing domain and the context of the application.Preliminary results are presented on the labellisation of the blood vessels on a digital angiography. Part of this work has been supported under Esprit project P26 “Advanced algorithms and architectures for speech and image processing”.     

An artificial intelligence membrane to detect network intrusion

We propose an artificial intelligence membrane to detect network intrusion, which is analogous to a biological membrane that prevents viruses from entering cells. This artificial membrane is designed to monitor incoming packets and to prevent a malicious program code (e.g., a shellcode) from breaking into a stack or heap in a memory. While monitoring incoming TCP packets, the artificial membrane constructs a TCP segment of incoming packets, and derives the byte frequency of the TCP segment (from 0 to 255 bytes) as well as the entropy and size of the segment. These features of the segment can be classified by a data-mining technique such as a decision tree or neural network. If the data-mining method finds a suspicious byte sequence, the sequence is emulated to ensure that it is just a shellcode. If the byte sequence is a shellcode, the sequence is dropped. At the same time, an alert is communicated to the system administrator. Our experiments examined seven data-mining methods for normal and malicious network traffic. The malicious traffic included 114 shellcodes, provided by the Metasploit framework, and including 10 types of metamorphic or polymorphic shellcodes. In addition, real network traffic involving shellcodes was examined. We found that a random forest method outperformed all the other datamining methods and had a very high detection accuracy, including a true-positive rate of 99.6% and a false-positive rate of 0.4%.     

The artificial intelligence approach to pattern recognition—a perspective and an overview

The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach. The conventional approach comprises two methodologies—statistical and structural. The paper reviews salient issues in the application of conventional techniques for extraction of information. The systems that use the artificial intelligence approach are characterized with respect to three key properties. The basic differences between the approaches and the computational aspects are reviewed. Current trends in the use of the AI approach are indicated. Some key ideas in current literature are reviewed.     

Biological and artificial intelligence

The paper discusses the characteristics of Biological Intelligence (BI) and its differences with artificial intelligence. In particular the plasticity of the nervous system is considered in the different forms with special attention to deterministic and localizationist views of the brain vs holistic approaches. When memory and learning are considered the localizationist views do not offer a possible solution to a number of problems while memory may be better conceptualized in terms of categorization procedures and generalizing strategies. Finally, the problem of individual variability, an important feature in terms of BI, is considered. The legitimacy of analogies between BI and AI is discussed and the necessity for an innovative approach to the field of AI is stressed.     

An approach for the integration of swarm intelligence in MAS: An engineering perspective

For more than 20 years, researchers have designed models in order to describe swarm intelligence and apply the resulting techniques to complex problems. However, there is still a gap between these models and current MAS methodologies. The goal of this paper is to propose a principled and methodological approach for the engineering of systems based upon swarm intelligence. The constraints are, on the one hand, to enable the analysis, design and implementation of such systems; and, on the other hand, to formally analyze and verify properties of resulting systems. The principles of the approach are based, on the one hand, on requirement driven activities that produce goals to be fulfilled by the system of interest and, on the other, hand on an ontological modeling of the problem domain. This ontological modeling conceptualizes the phenomenon one seek to imitate and thus allows it understanding. The produced ontology is refined through the methodology activities down to organizational models.