Toward Self-Referential Autonomous Learning of Object and Situation Models

Damerow, Florian; Knoblauch, Andreas; Körner, Ursula; Eggert, Julian; Körner, Edgar

doi:10.1007/s12559-016-9407-7

Cited by 4 publications

(5 citation statements)

References 44 publications

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“…Another example is the work in [ 17 ], in which the authors propose a brain-inspired cognitive architecture for autonomous learning of knowledge representation. This architecture presents key concepts in terms of acquiring knowledge based on behavioral needs and reusing patterns to explain new situations.…”

Section: Introductionmentioning

confidence: 99%

Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning

et al. 2018

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Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network.

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Section: Introductionmentioning

confidence: 99%

Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning

et al. 2018

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“…Importantly, the central role of astrocytes in information processing [5] has been envisaged decades ago by Galambos in his "glial-neuronal theory of the brain" [53]. Finally, my hypothesis that the astrocyte functions as a mediator for self-reflexive agents implies that it is able to interpret the environment only in terms of "its own" previously acquired situation models and has to integrate previous and novel models in a consistent behavior-related way [54]. Considering proemial counting it represents a self-reflexive or self-referential operation.…”

Section: Discussionmentioning

confidence: 88%

The Astrocyte as a Mediator for Self-Reflexive Agents

Mitterauer¹

2017

ABB

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A model of synapse-astrocyte interactions is proposed which enables repeated neuron-to-neuron connections from the single synapse to the network level. Specifically, the possibility that astrocytes may be organized in networks and processes of a single astrocyte may enable intracellular signaling loops via gap junctions is suggested as a plausible biophysical correlate for hierarchical signaling organization of cyclic pathways. This process ultimately translates to abstract planning, intention and execution of complex actions. The formalism applied is called proemial counting and it enables the generation of cycles of various length in the astroglial network, interpreted as intended action programs. Furthermore, the implementation of a model of the reticular formation in a robot brain based on glial-neuronal interactions is suggested. Finally, the implementation of robot brains with self-reflexive capabilities is discussed.

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“…Importantly, Haikonen [67] showed that a mismatch between behavioral outcome according to current sensory input signals and the expected outcome based on previously learned internal models triggers an adaptation of object and situation models. Moreover, Damerov and coworkers [66] developed a model of self-referential autonomous learning applying concepts similar to those in my proposed model [68]. Accordingly, a situation is rather the task-driven interpretation of a scene referring also to behavioral models, action outcomes, and internal states of the subject, such as intentions and goals…”

Section: Discussionmentioning

confidence: 97%

“…Approaches to implement brain-inspired architectures for autonomous agents are faced with the problem of adapting behavioral needs not anticipated by the human modeler [65] [66]. Importantly, Haikonen [67] showed that a mismatch between behavioral outcome according to current sensory input signals and the expected outcome based on previously learned internal models triggers an adaptation of object and situation models.…”

Section: Discussionmentioning

confidence: 99%

“…Importantly, both feedback expectation and expectation specialization are applied in explaining and predicting external feedback. If a different feedback instead of the expected occurs, expectation specialization is performed [66]. The concept of feedback expectation may correspond to feedforward mechanisms in astrocyte domains and their tripartite synapses.…”

Section: Discussionmentioning

confidence: 99%

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Astrocyte-Synapse Receptor Coupling in Tripartite Synapses: A Mechanism for Self-Observing Robots

Mitterauer¹

2018

ABB

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A model of an intentional self-observing system is proposed based on the structure and functions of astrocyte-synapse interactions in tripartite synapses. Astrocyte-synapse interactions are cyclically organized and operate via feedforward and feedback mechanisms, formally described by proemial counting. Synaptic, extrasynaptic and astrocyte receptors are interpreted as places with the same or different quality of information processing described by the combinatorics of tritograms. It is hypothesized that receptors on the astrocytic membrane may embody intentional programs that select corresponding synaptic and extrasynaptic receptors for the formation of receptorreceptor complexes. Basically, the act of self-observation is generated if the actual environmental information is appropriate to the intended observation processed by receptor-receptor complexes. This mechanism is implemented for a robot brain enabling the robot to experience environmental information as "its own". It is suggested that this mechanism enables the robot to generate matches and mismatches between intended observations and the observations in the environment, based on the cyclic organization of the mechanism. In exploring an unknown environment the robot may stepwise construct an observation space, stored in memory, commanded and controlled by the intentional self-observing system. Finally, the role of self-observation in machine consciousness is shortly discussed.

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Toward Self-Referential Autonomous Learning of Object and Situation Models

Cited by 4 publications

References 44 publications

Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning

Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning

The Astrocyte as a Mediator for Self-Reflexive Agents

Astrocyte-Synapse Receptor Coupling in Tripartite Synapses: A Mechanism for Self-Observing Robots

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