Make interacting and continuing behavior work as a whole

When a controller is assembled from separately developed components, test the actual combinations from which each component receives credit. Chapter 7 develops cooperative neural components, teams, adapting opponents and local cellular rules. A component that works with one partner may fail with another. Construct the partner selection, shared observations, action interface and credit relation before interpreting its fitness. C.40:4.5/.6 supplies the general combination and adaptive-trial connections. For competition, retain appropriate earlier or alternative opponents when a victory over the current one could conceal lost ability; a changing opponent also changes the meaning of the comparison.

If successful joint action depends on information available only to one partner, signaling and a shared convention may be needed. When each actor already observes enough to perform the task, communication can be unnecessary. Inherited coordinated responses and a code acquired from partners require different constructions. In Li and colleagues’ learned-communication construction, retain older agents who carry the acquired code, and let newborns learn through actual rewarded interaction with them. The newborns inherit parameters governing learning: memory size, reward discount, decay and the threshold for fixing an established response. Their acquired policy maps and event memories start empty; the parents’ learned maps are not copied into them.

Perform the generation in order: newborns first interact with both parents; then the population socializes across pairs; rewards accumulated during socializing rank agents for survival. In this construction, the 25 best agents whose lives span fewer than four generations become the next seniors. They retain their acquired responses and provide parents and partners for new learners. For the actual associative update, return to the source’s Real Time Learning and Evolution: a learner unit maps input patterns to activation parameters, records input-output events and changes the corresponding parameters from discounted reward, while potentiation protects established responses from unstable newcomer feedback. Its population, trial and reproduction rules supply the conditions in which that update obtains a shared code. Keep this performed learning and the surviving carriers alongside the inherited learning parameters. If the partners, informative feedback or learning operation are unavailable, obtain that missing contribution before claiming transmission; a description of the learner cannot supply a population carrying a convention.

A local update rule offers another whole: initialize a cellular state, repeatedly apply the rule using its permitted neighborhood information, obtain a larger structure or functioning system, and assess its behavior. Vary continuation time or apply a disturbance when persistence or recovery matters. A snapshot resembling the target can be transient; successful recovery can depend on information or actuation absent after a different injury. Retain the rule and initialization needed for renewed growth, or the live state needed for continuation, according to the actual receiving task. The source’s neural cellular-automata sections specify those operations; the desired whole is not obtained by merely naming its cells.

Body changes can invalidate acquired skills. The ESP extension gives a concrete construction: change morphology together with a new body-affecting skill, re-evaluate the protected older skills, reject excessive losses, then hold the selected morphology fixed while older controllers adapt to it. Its prescribed syllabus and skill interfaces supply the dependencies; further skill composition uses the resulting body. This can obtain a new capability while retaining needed earlier ones, at the cost of those repeated trials. Chapter 9 opens related questions about reachable further development, coupled environments and solutions, and changes of organization. A richer successor or a finite recovery result supports its tested continuation; it does not establish unlimited innovation.

For continued discovery, inspect what a retained basis can actually develop into. Try a feasible further change, obtain its quality and difference, and use those results to decide which basis merits continuation. Preserving current behavior and preserving future possibilities can lead to different choices. C.40:4.11/.12 explains that general return; the neural encoding, body, challenge generator or interaction supplies the particular possibilities. If an adequate current result serves the work, continued discovery can remain a separate purpose.