An Open Architecture for Affective Traits in a BDI Agent

Author(s):  
Bexy Alfonso ◽  
Emilio Vivancos ◽  
Vicent J. Botti
2005 ◽  
Author(s):  
D. Boroyevich ◽  
F. Wang ◽  
F. C. Lee ◽  
W. G. Odendaal ◽  
S. Edwards

2011 ◽  
Author(s):  
Brian Womble ◽  
William Schmidt ◽  
Mike Arendt ◽  
Tim Fain

Author(s):  
Kinga Kaleta ◽  
Justyna Mróz

AbstractAlthough women are believed to be more forgiving than men, the results of many studies comparing women with men vary. Moreover, little is known about unique correlates or differential patterns of experiencing forgiveness by gender. In the present study, we compared men and women in terms of their level of dispositional forgiveness and its emotional correlates, namely positive and negative affect, anxiety, and emotional control. The sample consisted of 625 individuals aged 19–69, of whom 478 (76.5%) were women and 147 (23.5%) were men. Polish versions of the Heartland Forgiveness Scale (HFS), the Positive and Negative Affect Schedule (PANAS), the Courtauld Emotional Control Scale (CECS), and the State-Trait Anxiety Inventory (STAI) were used. Men showed a higher level of general forgiveness and greater willingness to overcome unforgiveness than women, but there was no significant difference in positive facets of the disposition to forgive. In both genders negative affect, anxiety, and control of anger and of depression were negatively related to dimensions of dispositional forgiveness, and positive affect was positively associated with forgiveness. In females control of anxiety was negatively and in males it was positively related to facets of forgiveness. Gender moderated a number of links between affective traits and forgiveness of self and of situations beyond control, but not forgiveness of others.


2021 ◽  
Vol 10 (3) ◽  
pp. 42
Author(s):  
Mohammed Al-Nuaimi ◽  
Sapto Wibowo ◽  
Hongyang Qu ◽  
Jonathan Aitken ◽  
Sandor Veres

The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.


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