# Information, Thermodynamics and Life: A Narrative Review
## 3 Applications \"after\" Information: To the Process of Learning
### Learning and Its Application in Information-Driven, Complex Artificial Life  
Information theory has lately been the point of attention for researchers who are interested in the self-organization of artificial life or complex behavior. A concept of these approaches is derived from the notion that living organisms are information processing systems and in particular, knowledge processing systems. Recent research has also shown that the optimization of knowledge acquisition might be an evolutionary advantage. Although these ideas are quite intriguing, much interest is focused on the question how a general principle can be found for acquiring an artificial system with an internal drive for innovation or curiosity, or even more complicated with self-awareness. These approaches concern an artificial system that is characterized by a craving for increasing access to information about itself and the environment [54]. Sooner or later, a strategy for an open-ended, self-determined development of the artificial system will emerge [54]. The use of a proper measure for information was directly made clear by the development of information-theoretic approaches. Maximum randomness processes, like noise, could be favored by the attempt to maximize [PERSON]'s information, making it, therefore, an indirectly feasible choice. Nevertheless, taking into account the fact that the behavior of an artificial system can be as random as possible, this would be an optimal solution. Alternatively, the Kolmogorov complexity is a factor that could be used. [PERSON]'s approach to artificial curiosity and self-motivation was based on this factor. In addition, a set of a univariate and multivariate statistical set was introduced by [PERSON] et al. (2005), for the information structure quantification in motor and sensory channels [54; 55]. In terms of emphasis, the generic criteria of information theory could vary. The different forms of focus, appear in the following examples: on maximization of empowerment; lack of homogeneity reduction over multiple agents' states, evaluated either with Boltzmann entropy states of swarm-bots, or with Shannon entropy of rule-space difference; spatiotemporal coordination increases into a modular intelligent robot, evaluated by the exceeding entropy calculated through a variable time series states of modules, and so on. All these examples of self-organization driven by information is the appraisal of the perception-action loop (or sensorimotor) with regard to information theory. For example, the amount of Shannon information from the environment that one agent is able to inject within its sensors through actions completion, influencing future perceptions and actions, is evaluated by empowerment. In technical terms, for the behavior of a predetermined agent, the definition of the empowerment is stated as the agent's motivation channel capacity: the channel's max Mutual Information (MI), over the host of potential distributions of actions (i.e., the transmitted signal). At the same time, the exceeding entropy's maximization throughout a time period, stated in [PERSON] et al. (2006), permits the change of logic of controllers (i.e., agent's behavior changing) into a modular robot in a manner that manages to coordinate its actuators [54; 56]. In nature, autonomy is an ambiguous phenomenon, as well as a significant dare in the artifacts world. One of the main characteristics of autonomy for both artificial and natural systems is noticed in the ability of autonomous investigation [57]. In the human and animal world, the capability to modify their own way of operation apart from a requiredfeature for survival and adaptation into conditions, it also offers a knowledge system with new information in order to improve its intellectual efficiency and development. Effective investigation in high-dimensional spaces is a significant challenge in the creation of learning systems. The well-known investigation-exploitation reciprocity was thoroughly studied concerning the field of augmented learning.