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Agents, Objects, and Frames
Published in Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2021
The CPS framework is a top-down model for agent cooperation. As in the BDI model, an agent’s intentions play a key role. They determine the agent’s personal behavior at any instant, while joint intentions control its social behavior (Bratman 1987). An agent’s intentions are shaped by its commitment, and its joint intentions by its social convention. The framework comprises the following four stages, also shown in Figure 4.6, after which the team disbands and Stage 1 begins again:Recognition. Some agents recognize the potential for cooperation with an agent that is seeking assistance, possibly because it has a goal that it cannot achieve in isolation.Team formation. An agent that recognized the potential for cooperative action at Stage 1 solicits further assistance. If successful, this stage ends with a group having a joint commitment to collective action.Plan formation. The agents attempt to negotiate a joint plan that they believe will achieve the desired goal.Team action. The newly agreed plan of joint action is executed. By adhering to an agreed social convention, the agents maintain a close-knit relationship throughout.
AGC Design Using Multiagent Systems
Published in Hassan Bevrani, Takashi Hiyama, Intellyigent Automatic Generation Control, 2017
Hassan Bevrani, Takashi Hiyama
Over the years, various approaches to implement autonomous intelligent agents, such as belief-desire-intention (BDI) agents, reactive agents, agents with layered architectures,13 and agents implemented using model-based programming,14 have been introduced. The BDI approach is based on mental models of an agent’s beliefs, desires, and intentions. It considers agents to have beliefs (about itself, other agents, and its environments), desires (about future states), and intentions (about its own future actions). Reactive agents are normally associated with the model of intelligence. The fundamental property of reactive agents is that they do not perform reasoning through interaction with the environment. Instead, they react to inputs from their environment and messages from other agents.13
Self-Adaptive Cyber-City System
Published in Ricardo Armentano, Robin Singh Bhadoria, Parag Chatterjee, Ganesh Chandra Deka, The Internet of Things, 2017
Iping Supriana, Kridanto Surendro, Aradea Dipaloka, Edvin Ramadhan
Based on the model of the representation of system in Section 13.2.1, the construction model of developed software in the IoT concept, referring to the seven pillars of the taklif system, is mapped to the BDI Model (Bratman, 1987; Mora, 1999; Russel, 2003). A more detailed description can be found in the paper (Agustin and Supriana, 2012). The BDI Model is a theory of reasoning from a practical standpoint (practical reasoning) and were adopted in an agent-oriented architecture.
Going beyond BDI for agent-based simulation
Published in Journal of Information and Telecommunication, 2019
Agent-Oriented Programming (AOP) is a programming paradigm where programs are composed of agents. Similar to objects in Object-Oriented Programming (OOP), agents maintain a mental state and react to input by performing actions and changing their mental state. Some agents are also assumed to be intelligent agents, meaning that they pursue goals and exhibit social behaviour by communicating with other agents. Agent programming languages are programming languages that are designed for development of multi-agent systems with AOP. Examples of platforms that use agent programming languages include Agent-0 (Shoham, 1993), 3APL (Hindriks, De Boer, Van Der Hoek, & Meyer, 1999), 2APL (Dastani, 2008), Jason (Bordini, Hübner, & Wooldridge, 2007), JACK (Busetta, Ronnquist, Hodgson, & Lucas, 1999; Winikoff, 2005) and GOAL (Hindriks, 2009). The notions of belief, desire and intention (BDI) are key components in these languages, as they respectively denote what the agent believes, what the agent would like to achieve, and what the agent is currently working towards achieving. Formalizations of a BDI model in modal logics provide syntax and semantics for the model. Thus logic provides a theoretic framework for specification and verification of agent programs. In particular, work in the AOP community has resulted in frameworks and meta-models for Multi-Agent-Oriented Programming (MAOP).
Dynamic distributed decision-making for resilient resource reallocation in disrupted manufacturing systems
Published in International Journal of Production Research, 2023
Mingjie Bi, Ilya Kovalenko, Dawn M. Tilbury, Kira Barton
The belief-desire-intention (BDI) architecture has been widely used to provide a modular framework to design intelligent agents (Howden et al. 2001). Following the BDI design, the model of an RA in the authors' previous work is contained in the beliefs segment of the architecture within this work. In this work, we reformulate the structure and content of the belief section of the RAs as the desires and intentions are developed and integrated into the Knowledge Base. As shown in Figure 3, several aspects of the Knowledge Base are initialised before the manufacturing system begins operating. We assume this initialisation is completed by the manufacturers based on the customer order, physical layer, and initial production schedule.
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
The literature review identified that BDI agents are able to explain their actions using their beliefs and goals due to the reasoning process utilised by BDI agents using their cognitive mental state. Therefore, we chose to extend a BDI-based cognitive agent architecture FAtiMA (Fearnot AffecTIve Mind Architecture) (Dias, Mascarenhas, and Paiva 2014) to create an ECA that is able to provide belief and goal-based explanations, described in Section 3.1. Section 3.2 describes the study materials including the agent’s dialogue design. Section 3.3 presents the study design including procedure and data collection.