![netlogo setxy netlogo setxy](https://i.ytimg.com/vi/XB4e-3nws1g/maxresdefault.jpg)
We defined the values of reference as follows: "move forward": 5 "bump wall": -10 "turn": -3 "touch empty square": -1, "touch wall": -2.
![netlogo setxy netlogo setxy](https://live.staticflickr.com/6178/6167029554_899d4cb99c_z.jpg)
The Small Loop Problem thus consists of implementing a mechanism that tends to enact interactions with high values and to avoid interactions with negative values. These values are fixed and defined by the experimenter. The agent's self-motivation is defined by values associated with interactions. For instance, the interaction labeled "touch front wall" could mistakenly be relabeled "turn left" in a new experiment, and still result in the same behavior. Minimal initial presupposition implies, in particular, that the interpretation of interactions must not be hard-coded in the agent but rather learned through experience. The Small Loop Problem consists thus of implementing an agent capable of "smart" organization of behavior through the ten available interactions afforded by the Small Loop Platform (4 actions * 2 possible feedback values + 2 "turn" actions * 1 constant feedback).īy "smart" organization of behavior, we mean behavior consisting of autonomously discovering, learning, and exploiting regularities of interactions to satisfy the agent's preferences, with minimal initial presupposition of the environment being encoded in the agent. The agent has no other way of "perceiving" the environment than this single bit received when enacting these interactions.
![netlogo setxy netlogo setxy](http://mikecat.org/netlogo/images/penta140423a-2.png)
Each interaction returns a single bit feedback to the agent that tells whether the agent interacted with a wall or an empty square (i.e., "step" vs "bump", "touch wall" vs "touch empty" "turn" actions return a constant binary value). The Small Loop Platform offers six possibilities of action: try to move one square forward (succeed if the square ahead is empty), turn 90° left, turn 90° right, touch front square, touch left square, touch right square. This environment, together with a set of predefined "possibilities of interaction" afforded to the agent, form the "Small Loop Platform". The Small Loop Environment is the environment displayed in this model. This challenge consists of implementing an artificial agent that would "smartly" organize its behavior through autonomous interaction with the "Small Loop Environment". The Small Loop Problem is a challenge that we submit to the community of artificial developmental cognition. This NetLogo model provides a platform to investigate the "Small Loop Problem". More information at the imos-netlogo forge. Or simply watch the demo video on our blog.ĭownload the model file: Ernest_ogo ĭownload the imos.jar and ernest.jar that constitute the IMOS extension. Please try another browser, or run it on your computer, If it does not appear then it probably has a compatibility problem with your browser. It was successfully tested with Firefox 22.0 and Chrome 28.0. The Java applet should appear above this line. The Small Loop Challenge The Small Loop Challenge