Principles of holistic control

Followup to: Where map meets the territory, Levels of structure, Keeping the target in sight, Vague questions and precise answers, Causal rules and unpredictable actions.

When mind is supporting a picture of environment, it is not being passive. Some of the future events in the environment are determined by this picture, they happen because they are in the picture. The chain of events flows from representation in the mind to the outcome through the actions, which lie on the both sides, in the mind and in the environment at the same time.

It is trivial that whatever state the low-level output event assumes in the mind, the same state will appear in reality, because it is the same event on both sides. For other events in the mind, it is not necessarily so; changing such events in the mind will make them represent environment incorrectly, rather than making them determine corresponding state of environment. On the other hand, every change that does leave representation correct, leads to the environment complying with it.

This perspective can be turned around: if representation is restricted to only assume correct states, it can be freely varied within that bound, and whatever picture of environment it chooses to draw will automatically come true.

One way of constructing an accurate picture of environment is through following the rules of thumb, filling the gaps in the picture based on known elements of any kind. When it is known that there is a chair in a room, it is possible to infer that the chair is likely to stand there, instead of flying in the midair, even though original description doesn’t include information about how the room floor relates to the chair.

Since this picture is centered around the actions of the agent that supports it, rules of thumb need to be sufficiently strong (if not individually then cumulatively) not to break down under the surprising changes of context that may result from the actions.

Each element of the representation asks a question, adds a constraint on the set of possible causal patterns that satisfy it. Additional elements help filling the gaps in the model of environment, initiating inference that weaves the structure. Not all details of the environment can be supported in the mind at once, even if they can be inferred from known facts. A tiniest hint may bring to attention many precise details, reconstructing intermediate elements of structure. Inferred from robust rules of thumb, state of mind would indirectly correspond to the state of environment, and giving different hints will lead to the state of mind corresponding to different aspects of environment.

The uncertainty about the future state of environment that is determined by the state of mind may be resolved in many ways, by assuming one of the allowed states of mind. On the side of the mind, the process of resolution of this uncertainty starts from making a decision, from introducing a fact in the model that doesn’t otherwise follow, and checking if the model assembles to a coherent state, if this element leaves the picture in the mind corresponding to environment, thus causing the environment to assume the state corresponding to the decision. If the decision consists in a relatively vague hint about the future, there are good chances that there is in fact a state of environment that satisfies the hint.

Much like drawing the attention to an aspect of environment, asserting a certain vague property in the future state of environment leads to construction of the detailed representation of the state of environment that has that property, driven by a multitude of specific inferred facts about the state of environment (both in the past and in the future) and general direction specified by the property. Where attention draws the details of representation from available factual information, model of the future may make up some of the details when they can be determined by the model. Thus constructed model will include events in the past and the future, but also the present, in particular low-level action events. Choosing a certain property in the future leads to construction of the model of environment that has that property, and the model of environment includes specific state of low-level actions in the present, which causes these actions to be carried out, which in turn determines the future to have required property, to be in accordance with the model.

The plan formed by the model of the future chosen to lead to a certain outcome doesn’t need to be very detailed, for example it doesn’t need to contain the whole sequence of low-level actions from the current point on. Plan gets refined as it unwinds, as more accurate factual information becomes available about events in the environment that were only modeled based on the goal at the start. At each moment, low-level action is chosen according to the best current guess included in the current model.

This approach allows to view the process of control in intelligent agent as a result of two cognitive pressures acting on representation of environment supported by its mind. The first pressure compels the representation in the mind to be correct, to depict the state of environment (past, future and present) as accurately as possible. The second pressure biases the representation to see the future state of environment that is as close as possible to the goal.

I call this perspective on how control algorithm could operate “holistic control”, to reflect the way plans get constructed. Inference operates across the levels of representation and in both directions in time, it is neither bottom-up nor top-down, it is not forward chaining or backward chaining. Control algorithm doesn’t contain clear-cut feedback loops, processing doesn’t happen in feed-forward fashion. The model of environment is held together by heuristic rules that aren’t organized in any kind of hierarchy, the model itself is “flat”, not modular except for the structure inherited from environment it represents. The operation of control algorithm is focused on the support of model of environment, not on action and perception. Action and perception are only peripheral (although indispensable) aspects of control, with low-level input binding the model of environment to reality at one tiny point, supplying new facts and showing the mistakes, and low-level output giving the model ability to participate in the causal web of environment.