BDI Agent-Based Mobile Assistant Service on Android Using JAM

2011 ◽  
Author(s):  
Hyo-eun Eom ◽  
Seheon Song ◽  
Minkoo Kim
Keyword(s):  
Agent Based ◽  
Bdi Agent

Exploring Agent-Based Modeling for Emergency Logistics Collaborative Decision Making

2013 ◽  
Vol 710 ◽  
pp. 781-785 ◽  
Author(s):  
Li Zhang ◽  
Zhi Qi ◽  
Hao Cui ◽  
Sen Hua Wang ◽  
Ya Hui Ning ◽  
...  
Keyword(s):  
Decision Making ◽  
Local Level ◽  
Collaborative Decision Making ◽  
Agent Based ◽  
Concept Model ◽  
Emergency Logistics ◽  
Bdi Agent ◽  
Distribution Process ◽  
Multi Agent ◽  
Collaborative Decision

Aiming at the requirements of urgency and dynamics in emergency logistics, this paper presents a multi-agent system (MAS) concept model for emergency logistics collaborative decision making. The suggested model includes three kinds of agents, i.e., role agent, function agent and assistant agent. Role agent excutes emergency logistics activities, function agent achieves the task requirements in every work phase and assistant agent helps organizing and visiting data. Two levels agent views serve as the basic skeleton of the MAS. Top level is the global decision-making view, which describes the task distribution process with multiple agents. Local level is the execution planning view, which simulates task executing process of the performer. Finally, an extended BDI agent structure model is proposed to help the implementation at application level.

scholarly journals DESIGNING A BDI AGENT REACTANT MODEL OF BEHAVIOURAL CHANGE INTERVENTION

2015 ◽  
Vol 78 (2-2) ◽  
Author(s):  
Ojeniyi Adegoke ◽  
Azizi Ab Aziz ◽  
Yuhanis Yusof
Keyword(s):  
Behavioural Change ◽  
Psychological Reactance ◽  
Main Determinant ◽  
Agent Based ◽  
Agent Model ◽  
Design And Implementation ◽  
Psychological Theories ◽  
Bdi Agent ◽  
Behavioural Factors ◽  
Generic Representation

Belief-Desire-Intention (BDI) model is well suited for describing agent’s mental state. The BDI of an agent represents its motivational stance and are the main determinant of agent’s actions. Therefore, explicit understanding of the representation and modelling of such motivational stance plays a central role in designing BDI agent with successful behavioural change interventions. Nevertheless, existing BDI agent models do not represent agent’s behavioural factors explicitly. This leads to a gap between design and implementation where psychological reactance has being identified as the cause of BDI agent behavioural change interventions failure. Hence, this paper presents a generic representation of BDI agent model based on behavioural change and psychological theories. Also, using mathematical analysis the model was evaluated. The objective of the proposed BDI agent model is to bridge the gap between agent design and implementation for successful agent-based interventions. The model will be realized in an agent-based application that motivates children towards oral hygiene. The study explicitly depicts how agent’s behavioural factors interact to enhance behaviour change which will assist agent-based intervention designers to be able to design intervention that will be void of reactance.

scholarly journals On the Testability of BDI Agent Systems

2014 ◽  
Vol 51 ◽  
pp. 71-131 ◽  
Author(s):  
M. Winikoff ◽  
S. Cranefield
Keyword(s):  
Adaptive Systems ◽  
Software System ◽  
Bdi Agents ◽  
Complex Behaviour ◽  
Agent Based ◽  
Agent Systems ◽  
Failure Handling ◽  
Bdi Agent

Before deploying a software system we need to assure ourselves (and stakeholders) that the system will behave correctly. This assurance is usually done by testing the system. However, it is intuitively obvious that adaptive systems, including agent-based systems, can exhibit complex behaviour, and are thus harder to test. In this paper we examine this "obvious intuition" in the case of Belief-Desire-Intention (BDI) agents. We analyse the size of the behaviour space of BDI agents and show that although the intuition is correct, the factors that influence the size are not what we expected them to be. Specifically, we found that the introduction of failure handling had a much larger effect on the size of the behaviour space than we expected. We also discuss the implications of these findings on the testability of BDI agents.

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