We define a self-programming agent as an agent that can autonomously acquire executable code and re-execute this code appropriately.
Similar to other machine learning agents, self-programming agents record data in memory as they learn. Traditional machine learning agents, however, run a predefined program that exploits this data as parameters. Self-programming agents also run a predefined program, but this program can control the execution of learned data as sequences of instructions.
To understand the full implication of this definition, it is important to take a cognitive science perspective rather than a software development perspective. A natural cognitive system (an animal) does not have a compiler or an interpreter to exploit a programming language. The only thing at its disposal that remotely resembles an instruction set is the set of interaction possibilities it has with the world around it. The only thing at its disposal that remotely resembles an execution engine is its cognitive system which allows it to execute and learn sequences of interactions with the world.
We draw inspiration from natural cognitive systems to design self-programming agents. Like biological systems, these agents program themselves using the instruction set at their disposal (the set of interaction possibilities they have with the world), and the execution engine at their disposal (their cognitive system that executes and learns sequences of interactions with the world).
There are profound theoretical reasons why self-programming is decisive to achieve artificial intelligence. Page 45 will refer you to some articles that elaborate on these reasons, specifically coming from the theory of enaction.
Nevertheless, we can already give a simple and intuitive answer. If we build two identical robots, it would be no fun if these two robots generated similar behaviors in the same circumstances. As they develop, we would like them to assess situations differently, make different choices, and carry out different behaviors. Only when we see these significant behavioral differences emerge will we be willing to consider each robot as an intelligent being as opposed to a mere automaton.
Since assessing the situation, making choices, and carrying out behaviors is the role of programs, we will only manage to make significant behavioral differences emerge autonomously if the robot can program itself autonomously.
Of course, defining "significant behavioral differences" is challenging. We recorded Video 41 to give an example. It was already presented in the IDEAL MOOC home page, but we invite you to watch it again.
Video 41: Example of self-programming. These two agents implement the same initial algorithm. Yet, because they go through different individual experiences, they find different strategies to catch a prey.
As we said in the home page, self-programming raises many questions.
An obvious question is why the system should choose to learn a specific program rather than another. We must implement driving principles to give a direction to the system's development, without specifying a final goal so that the system can keep learning new programs indefinitely.
Interactional motivation, presented in Lesson 2, provides us with a possible answer to this challenge because it allows us to specify inborn behavioral preferences without specifying a predefined goal. An interactionally motivated self-programming agent will choose to learn programs that can help it enact interactions that have a high positive valence, while avoiding situations where interactions with a negative valence are likely to happen.
Interactional motivation is not the only principle that can drive self-programming, but it is the approach we will continue using in the examples in Lesson 4.
Constructivist epistemology, presented in Lesson 3, provides us with a possible answer to this question: the learned programs can come from the same place where all of the agent's knowledge comes from: regularities of interaction. This leads us to Lesson 4's key concept:
Self-programming consists of the re-enaction of regularities of interaction.
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