I have not been able to read the original but the abstract sounds very interesting. The paper is called Self-Consciousness for Artificial Entities using Modular Neural Networks and here is the abstract.
One of the most puzzling cognitive features is self-consciousness. A considerable amount of research has been conducted on this question in different fields like Psychology, Neurobiology and Cognitive Science. Self-consciousness implies not only self or group recognition, but also real knowledge of one’s own identity. In this paper, a cognitive architecture of self-consciousness for autonomous artificial entities (holons) is proposed. This cognitive architecture includes: abstraction, self-representation, other individuals’ representation, and action modules. It also includes a learning process of self-representation by direct (self-experience based) and observational learning (based on the observation of other individuals). For model implementation a new approach is taken using Modular Artificial Neural Networks (MANN) due to their biological inspiration, modularity and adaptability. We explain the cognitive architecture that enables dynamic self-representation. For model testing a multi-holon virtual environment was implemented. We analyse the effect of holon interaction, focusing on the evolution of the holon’s abstract self-representation. Finally, the results are explained and analysed and conclusions drawn.
So using computer programs that mimic groups of neurons this Spanish group lead by Milton Martinez Luaces have produced self-aware programs. ScienceDaily has an item on this research. (here)
.This research represents a dual advance in the modelling of awareness-associated cognitive functions. On the theoretical side, it applies the theory of informons and holons to awareness structures. An informon is an information entity. It can take the form of data, news or knowledge. The term holon refers to autonomous entities that act both as a part and as a whole.
On the practical side, awareness-associated processes were simulated and influenced the design and development of software models. To do this, modular neural networks were used to develop multi-entity simulators. The networks were used to simulate several scenarios of interaction between their own potential and that of the systems with which they interact.
In the case of human beings, self-awareness does not imply just an abstract image of what one is, but also an image of ones trajectory throughout time. This research also modelled the time dimension of self-awareness using time-delay neural networks. These networks have shown, in different interaction scenarios, that the image that each entity has of its qualities in the past or its expectations for the future has an impact on how it interacts with other entities. For example, a figure reveals how interaction with other artificial entities, in this case in a competitive scenario, enables these entities to develop self-awareness.
In the same way as individuals tend to form groups with common interests and, as a result, develop a sense of belonging at more than one level, artificial entities may interact similarly to achieve a particular purpose .
I hope more detail is available in future.