This article gives a novel approach to cooperative decision-making algorithms by Joint Action learning for the retail shop application. Accordingly, this approach presents three retailer stores in... Show More

This article gives a novel approach to cooperative decision-making algorithms by Joint Action learning for the retail shop application. Accordingly, this approach presents three retailer stores in the retail marketplace. Retailers can help to each other and can obtain profit from cooperation knowledge through learning their own strategies that just stand for their aims and benefit. The vendors are the knowledgeable agents to employ cooperative learning to train in the circumstances. Assuming a significant hypothesis on the vendor's stock policy, restock period, and arrival process of the consumers, the approach was formed as a Markov model. The proposed algorithms learn dynamic consumer performance. Moreover, the article illustrates the results of cooperative reinforcement learning algorithms by joint action learning of three shop agents for the period of one-year sale duration. Two approaches have been compared in the article, i.e. multi-agent Q Learning and joint action learning.

The research work presented in this article investigates and explains the conceptual mechanisms of consciousness and common-sense thinking of animates. These mechanisms are computationally simulated... Show More

The research work presented in this article investigates and explains the conceptual mechanisms of consciousness and common-sense thinking of animates. These mechanisms are computationally simulated on artificial agents as strategic rules to analyze and compare the performance of agents in critical and dynamic environments. Awareness and attention to specific parameters that affect the performance of agents specify the consciousness level in agents. Common sense is a set of beliefs that are accepted to be true among a group of agents that are engaged in a common purpose, with or without self-experience. The common sense agents are a kind of conscious agents that are given with few common sense assumptions. The so-created environment has attackers with dependency on agents in the survival-food chain. These attackers create a threat mental state in agents that can affect their conscious and common sense behaviors. The agents are built with a multi-layer cognitive architecture COCOCA (Consciousness and Common sense Cognitive Architecture) with five columns and six layers of cognitive processing of each precept of an agent. The conscious agents self-learn strategies for threat management and energy level maintenance. Experimentation conducted in this research work demonstrates animate-level intelligence in their problem-solving capabilities, decision making and reasoning in critical situations.

This article describes how in the VANET environment, routes are broken owing to node mobility. Moreover, the usage of wireless links for data communication leads to inherent unreliability and are... Show More

This article describes how in the VANET environment, routes are broken owing to node mobility. Moreover, the usage of wireless links for data communication leads to inherent unreliability and are error prone. Single path routing uses a prediction mechanism to compute a reliable path considering vehicle velocity and vehicle direction. Nevertheless, this methodology does not deal with major real-world traffic conditions. Hence, to address the aforementioned problems and to enhance reliability and fault tolerance, multipath routing protocols are employed. However existing multipath routing protocols even though compute multipath, only one path will be engaged in actual communication at any given time. Hence this work proposes Adaptive Congestion Controlled Multipath Routing in a VANET. The proposed work computes multiple paths from source to destination using cubic Bezier curves and more importantly, employs all/more than one path during the communication. The paths thus computed are adaptive in nature dependent upon the direction of mobility of source and destination vehicles.