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Production Management Methods
Published in Susmita Bandyopadhyay, Production and Operations Analysis, 2019
There are various kinds of agents, such as (Paolucci and Sacile, 2005): Collaborative Agents: Which emphasize autonomy and cooperationInterface Agents: Which are autonomous and utilize learning to perform tasks for their usersMobile Agents: Which are computational processes capable of moving through a network, interacting with foreign hosts, gathering information on behalf of the users, and returning to their users after performing their assigned dutiesReactive Agents: Which represent a special category of agents that do not possess internal, symbolic models of their environments, but instead act or respond according to stimuli arising from the environments in which they are embeddedHybrid Agents: Which are particular in that they combine two or more agent philosophies within a single agentHeterogeneous Agent: System, which refers to a collection of two or more agents with different agent architecture
Agent Architectures
Published in Weiming Shen, Douglas H. Norrie, Jean-Paul A. Barthès, Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing, 2019
Weiming Shen, Douglas H. Norrie, Jean-Paul A. Barthès
Recently, layered agent architectures have been described by a number of researchers. A layered agent architecture is usually organized with components for perception and action at the bottom and reasoning at the top. The perception feeds the reasoning subsystem, which governs the actions, including deciding what to perceive next (Huhns and Singh, 1998). Most layered agent architectures are hybrid architectures as discussed in Section 6.4.1.4. Layering is a powerful means for structuring functionalities and control, and thus is a valuable tool for system design supporting several desired properties such as reactivity, deliberation, cooperation, and adaptability. The main idea is to structure the functionalities of an agent into two or more hierarchically organized layers that interact with each other in order to achieve coherent behavior of the agent as a whole.Müller 1996
Changing users’ health behaviour intentions through an embodied conversational agent delivering explanations based on users’ beliefs and goals
Published in Behaviour & Information Technology, 2023
Amal Abdulrahman, Deborah Richards, Ayse Aysin Bilgin
FAtiMA is a cognitive and emotional agent architecture. The agent architecture includes three main parts: the emotional appraisal, the memory and the action selection unit. As we are building a conversational agent, the user–agent interaction is achieved through dialogue. The user responds to the agent by selecting an utterance from a dynamic list that changes according to the context. Hence, the agent perceives the user only through the user’s verbal response. These responses are then taken as input to the action selection unit and the agent’s memory. The agent memory includes the agent’s knowledge base (the agent’s beliefs and domain knowledge) and autobiographical memory where the agent stores copies of all the interactions with the user which is beneficial for long-term interactions. The agent decision-making process is done in the action selection unit using pre-defined logical rules. The agent’s actions are its utterances that are resultant of the action selection unit. For more details please refer to Dias et al. (2014).
Trust and Distrust based Cross-domain Recommender System
Published in Applied Artificial Intelligence, 2021
Proposed system is a trust and distrust-based cross domain context aware recommender system (TDCDCARS) that works in a multi-agent environment. A multi-agent architecture combines multiple agents to complete the assigned task either individually or with the help of other agents.