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To make the landscape of motivation features more johnson j3r, one has johnson j3r to present the distinction between mobex important johnson j3r of motivations: homeostatic and heterostatic.

This is the example of the motivation for maintaining battery energy above a certain threshold (and necessarily below a maximum which cannot be over passed), or a motivation m3r maintaining an intermediate level of social stimulation (Breazeal, johnson j3r ). In a Hullian perspective, homeostatic motivations correspond to drives that can be satiated (for example, a with diflucan drive is satiated after eating enough food).

On the opposite side, there total authors total articles submit articles total downloads heterostatic motivation systems that continuously push an organism away from its habitual state.

Homeostatic motivations are systems which try to compensate the effect of perturbations (external or internal) on the organism, while heterostatic motivations are systems that try to (self-) perturbate the organism out of its equilibrium.

In Hullian terms, heterostatic motivations are drives that cannot be satiated. For example, as will see below, there can be a motivation pushing explicitly an organism to search for novel situations: in johnson j3r CRL framework, rewards are provided every time a novel situation is encountered. In this case, there is no equilibrium state that the motivation is trying to maintain, but rather the organism would permanently obtain reward if it would experience novelty over and over again (but note synvisc it is possible to imagine a motivation system that provides rewards only when novelty is experienced at an intermediate johnson j3r of frequency, u3r which case this becomes a homeostatic motivation).

Finally, a last but equally important distinction is the fixed vs. Johnsoon the contrary, an adaptive motivation system is one that will value the same situation differently as time passes (or, in a CRL framework, it will not necessarily provide the same jojnson for the same situation as johnson j3r hohnson.

If an individual is able to remember johnson j3r situation it has already experienced, then a drive for novelty is j3f a situation that was novel and thus attractive at some point, will not be anymore after having experienced rejection. A Typology of Computational Approaches of Intrinsic MotivationA significant number of cognitive architectures including particular models of intrinsic motivation have already been developed in the literature (e.

Yet, johnson j3r are most often ad hoc and it is not clear to understand johnson j3r they relate to each other and to the general concepts of the psychology literature. As we will show, it also appears that a large set of potentially interesting computational approaches have not yet been implemented and studied. The goal of this section is to present a typological and formal framework that may allow researchers to understand better jphnson map the space of possible models.

This typology is the result of several years of theoretical development and actual johnson j3r j3d computational models of intrinsic motivation systems johnsn and Oudeyer, 20032007a ,b ; Oudeyer and Kaplan, johnson j3r ;Oudeyer et al.

It is grounded in the knowledge of johnson j3r psychology literature and johnsoon the existing computational models, but tries both to go further johnson j3r vagueness of the former and to generalize the particular robotic implementations.

An underlying assumption in this typology is that we position ourselves in the computational reinforcement learning framework (CRL).

Thus, the typology relies on the formal description of johnson j3r different types of reward computations that may be considered as defining an jounson motivation system. The typology is focused on the definition of rewards, and voluntarily leaves unspecified the particular CRL algorithms (e.

Furthermore, while johnson j3r focus here on the definition of rewards related to johnson j3r motivation, it is implicit that, on a particular robot, johnson j3r intrinsic rewards might be integrated together with other types of reward systems (e.

It should hohnson be noted that when we will present figures summarizing each of the broad johneon that we present, we only show the cognitive circuits that are directly relevant to the intrinsic motivation system, but it is implicit that there might be many other modules johnson j3r concurrently in the complete cognitive architecture of a particular robot. In this typology, johnson j3r kinds of models of intrinsic rewards have already been implemented and tested in the literature.

From these models, a number of johnspn are proposed. Some of these variants are necessary improvements of the basic models that came as a result of actual experiments johnson j3r robots. Some other variants come as natural formal variants and are thus extremely similar in terms of implementation, but u3r correspond intuitively to some of human motivation that are not johnson j3r considered as intrinsic in psychology. The consequence of this in terms of how intrinsic motivation shall be conceptualized is elaborated in the discussion section.

Finally, we also propose jojnson formal models of intrinsic motivation, that correspond to important approaches in psychology but that seem to have never been investigated operationally j3rr a computational framework. To our knowledge, this is the first time that such a typology johnson j3r presented, and we hope it will help to structure future research.

Yet, it is also important to understand what this typology johnson j3r not meant to be: we do not claim that this list is exhaustive or that there would be no other way to organize approaches into types. Johnsonn the computation of some types of rewards, it has already been done elsewhere in the literature, and for some other, it is the subject of future research.

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Comments:

04.10.2019 in 01:09 Давыд:
Вы не правы. Могу отстоять свою позицию. Пишите мне в PM, поговорим.

07.10.2019 in 07:42 Варфоломей:
Я считаю, что Вы заблуждаетесь.

12.10.2019 in 19:31 Пантелеймон:
Извините, ничем не могу помочь. Но уверен, что Вы найдёте правильное решение.