A bio-inspired model of behavior considering decision-making and planning, spatial attention and basic motor commands processes. (January 2020)

• Record Type: Journal Article
• Authors: Ramirez-Pedraza, Raymundo; Vargas, Natividad; Sandoval, Carlos; del Valle-Padilla, Juan Luis; Ramos, Félix
• Is Part Of: Cognitive systems research. Volume 59(2020)
• Page Start: 293
• Page End: 303
• Publication Date: 2020-01
• Subjects: Brain model -- Decision-making -- Planning -- Spatial attention -- Motor system -- Goal-driven; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2019.10.006
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: Cognitive architectures (CA ) are an IA approach to implement computer systems with human-like behavior. Fundamental exhibited human capabilities include planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or are restrained to too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. To try to alleviate the problem, we present a bio-inspired computational model to solve the autonomous navigation problem of a computational entity in a controlled context. This proposal is the result of the interaction between planning and decision-making, spatial attention and the motor cognitive functions. The proposed model is based on neuroscientific evidence concerning the involved cognitive functions and is part of a more general cognitive architecture. In the case study developed to validate our idea, we can see that the processes previously identified play an important role to accomplish spatial navigation. In the case study presented, an agent achieves the navigation over an unexplored maze from an initial to a final position successfully. The reunited results motivate us to continue improving our model considering attentional information to influence the agent's motor behavior.