Compact Internal Representation as a Functional Basis for Protocognitive Exploration of Dynamic Environments

Makarov, Valeri A.; Villacorta-Atienza, José Antonio

doi:10.5772/15127

Cited by 3 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our previous works (Villacorta-Atienza et al 2010;Makarov and Villacorta-Atienza 2011) we have proposed and developed cognitive navigation based on PfCA in deterministic dynamic environments, whose evolution only depends on the dynamical initial conditions of their elements, but can not be altered by the agent's behaviour. Thus in these situations any recursive interaction between the agent and its environment, as cooperation or competition, is impossible.…”

Section: Coexistencementioning

confidence: 99%

See 1 more Smart Citation

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps

2015

View full text Add to dashboard Cite

The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e., the agent's decisions depend on the decisions made by humans that in turn depend on the agent's decisions. To deal with this high-level cognitive skill, we propose a neural network architecture implementing Prediction-for-CompAction paradigm. The network predicts possible human-agent collisions and compacts the time dimension by projecting a given dynamic situation into a static map. Thereby emerging compact cognitive map can be readily used as a "dynamic GPS" for planning actions or mental evaluation of the convenience of cooperation in a given context. We provide numerical evidence that cooperation yields additional room for more efficient navigation in cluttered pedestrian flows, and the agent can choose path to the target significantly shorter than a robot treated by humans as a functional machine. Moreover, the navigation safety, i.e., the chances to avoid accidental collisions, increases under cooperation. Remarkably, these benefits yield no additional load to the mean society effort. Thus, the proposed strategy is socially compliant, and the humanoid agent can behave as "one of us."

show abstract

Section: Coexistencementioning

confidence: 99%

“…To predict trajectories we use a dynamic memory implemented in a so-called trajectory modeling neural network (TMNN, Appendix A.1) (Villacorta-Atienza et al 2010;Makarov and Villacorta-Atienza 2011).…”

Section: Prediction Of Object Trajectorymentioning

confidence: 99%

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps

2015

View full text Add to dashboard Cite

show abstract

Compact Internal Representation of Dynamic Environments: Simple Memory Structures for Complex Situations

Villacorta-Atienza

Velárde

Makarov

2013

Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II

View full text Add to dashboard Cite

Compact internal representation as a protocognitive scheme for robots in dynamic environments

Villacorta-Atienza

Salas²,

Alba³

et al. 2011

SPIE Proceedings

View full text Add to dashboard Cite

Animals for surviving have developed cognitive abilities allowing them an abstract representation of the environment. This Internal Representation (IR) could contain a huge amount of information concerning the evolution and interactions of the elements in their surroundings. The complexity of this information should be enough to ensure the maximum fidelity in the representation of those aspects of the environment critical for the agent, but not so high to prevent the management of the IR in terms of neural processes, i.e. storing, retrieving, etc. One of the most subtle points is the inclusion of temporal information, necessary in IRs of dynamic environments. This temporal information basically introduces the environmental information for each moment, so the information required to generate the IR would eventually be increased dramatically. The inclusion of this temporal information in biological neural processes remains an open question. In this work we propose a new IR, the Compact Internal Representation (CIR), based on the compaction of spatiotemporal information into only space, leading to a stable structure (with no temporal dimension) suitable to be the base for complex cognitive processes, as memory or learning. The Compact Internal Representation is especially appropriate for be implemented in autonomous robots because it provides global strategies for the interaction with real environments (roving robots, manipulators, etc.). This paper presents the mathematical basis of CIR hardware implementation in the context of navigation in dynamic environments. The aim of such implementation is the obtaining of free-collision trajectories under the requirements of an optimal performance by means of a fast and accurate process.

show abstract

Compact Internal Representation as a Functional Basis for Protocognitive Exploration of Dynamic Environments

Cited by 3 publications

References 21 publications

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps

Compact Internal Representation of Dynamic Environments: Simple Memory Structures for Complex Situations

Compact internal representation as a protocognitive scheme for robots in dynamic environments

Contact Info

Product

Resources

About