Agent-Based System for Mobile Service Adaptation Using Online Machine Learning and Mobile Cloud Computing Paradigm

An important aspect of modern computer systems is their ability to adapt. This is particularly important in the context of the use of mobile devices, which have limited resources and are able to work longer and more efficiently through adaptation. One possibility for the adaptation of mobile service execution is the use of the Mobile Cloud Computing (MCC) paradigm, which allows such services to run in computational clouds and only return the result to the mobile device. At the same time, the importance of machine learning used to optimize various computer systems is increasing. The novel concept proposed by the authors extends the MCC paradigm to add the ability to run services on a PC (e.g. at home). The solution proposed utilizes agent-based concepts in order to create a system that operates in a heterogeneous environment. Machine learning algorithms are used to optimize the performance of mobile services online on mobile devices. This guarantees scalability and privacy. As a result, the solution makes it possible to reduce service execution time and power consumption by mobile devices. In order to evaluate the proposed concept, an agent-based system for mobile service adaptation was implemented and experiments were performed. The solution developed demonstrates that extending the MCC paradigm with the simultaneous use of machine learning and agent-based concepts allows for the effective adaptation and optimization of mobile services

Multi-agent blackboard architecture for supporting legal decision making

Our research objective is to design a system to support legal decision-making using the multi-agent blackboard architecture. Agents represent experts that may apply various knowledge processing algorithms and knowledge sources. Experts cooperate with each other using blackboard to store facts about current case. Knowledge is represented as a set of rules. Inference process is based on bottom-up control (forward chaining). The goal of our system is to find rationales for arguments supporting different decisions for a given case using precedents and statutory knowledge. Our system also uses top-down knowledge from statutes and precedents to interactively query the user for additional facts, when such facts could affect the judgment. The rationales for various judgments are presented to the user, who may choose the most appropriate one. We present two example scenarios in Polish traffic law to illustrate the features of our system. Based on these results, we argue that the blackboard architecture provides an effecive approach to model situations where a multitude of possibly conflicting factors must be taken into account in decision making. We briefly discuss two such scenarios: incorporating moral and ethical factors in decision making by autonomous systems (e.g. self-driven cars), and integrating eudaimonic (well-being) factors in modeling mobility patterns in a smart city

Learning Agent for a Service-Oriented Context-Aware Recommender System in Heterogeneous Environment

Traditional recommender systems provide users with customized recommendations of products or services. They employ various technologies and algorithms in order to search and select the best options available while taking into account the user's context. Increasingly often, such systems run on devices in heterogeneous environments (including mobile devices) making use of their functionalities: various sensors (e.g. movement, light), wireless data transmission technologies and positioning systems (e.g. GPS) among others. In this paper, we propose an innovative recommender system that determines the best service (including photo and movie conversion) and simultaneously accommodates the context of the device in a heterogeneous environment. The system allows the choice between various service providers that make their resources available using cloud computing as well as having the services performed locally. In order to determine the best possible recommendation for users, we employ the concept of learning agents, which has not been thoroughly researched in connection with recommender systems so far

Multi-Platform Intelligent System for Multimodal Human-Computer Interaction

We present a flexible human--robot interaction architecture that incorporates emotions and moods to provide a natural experience for humans. To determine the emotional state of the user, information representing eye gaze and facial expression is combined with other contextual information such as whether the user is asking questions or has been quiet for some time. Subsequently, an appropriate robot behaviour is selected from a multi-path scenario. This architecture can be easily adapted to interactions with non-embodied robots such as avatars on a mobile device or a PC. We present the outcome of evaluating an implementation of our proposed architecture as a whole, and also of its modules for detecting emotions and questions. Results are promising and provide a basis for further development

A strategy learning model for autonomous agents based on classification

In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning proces

Introduction

The book has two goals. The first goal is to identify and assess trust in three aspects: organizational, technological, and psychological. The second goal is to use the research results as an introduction to further in-depth research focused on the future relationship between trust ideas and social robots. As a result, the monograph provides interpretations that can form the basis for more profound theoretical and practical applications

Artificial intelligence, management and trust

The main challenge related to the development of artificial intelligence (AI) is to establish harmonious human-AI relations, necessary for the proper use of its potential. AI will eventually transform many businesses and industries; its pace of development is influenced by the lack of trust on the part of society. AI autonomous decision-making is still in its infancy, but use cases are evolving at an ever-faster pace. Over time, AI will be responsible for making more decisions, and those decisions will be of greater importance.The monograph aims to comprehensively describe AI technology in three aspects: organizational, psychological, and technological in the context of the increasingly bold use of this technology in management. Recognizing the differences between trust in people and AI agents and identifying the key psychological factors that determine the development of trust in AI is crucial for the development of modern Industry 4.0 organizations. So far, little is known about trust in human-AI relationships and almost nothing about the psychological mechanisms involved. The monograph will contribute to a better understanding of how trust is built between people and AI agents, what makes AI agents trustworthy, and how their morality is assessed. It will therefore be of interest to researchers, academics, practitioners, and advanced students with an interest in trust research, management of technology and innovation, and organizational management