the following lays out a framework for an adaptive decision-making agent that constructs models of the world to behave intelligently and maximize its fitness.

models and decisions

models are structures that represent situational and behavioral patterns. actions that lead to changes in an environment are equivalent to state transitions or paths between differing scenarios. decision-making strategies select paths at each point to prompt actions that change the environment and thus move the current state to some alternative state.

alternatives and choices

alternatives are models that may or may not be held at the same time which represent situations that are mutually exclusive of one another. in other words, they contradict in some way such that the realization of one implies the negation of the other.

ultimately a selection decides on one alternative that is to be maintained while the others are disposed. models are chosen in order to maximize fitness. the best option is that which results in the highest fitness output relative to that of its alternatives.

plans and biases

plans are high-level representations of desired paths. a plan is formed by decision-making strategies that are based on inferences at higher levels regarding the decisions made at lower levels. top-down influences from plans to low-level decisions allow for intentional action to occur. in other words, influences guide decisions made at lower levels.

plans reduce the branching factor of decision trees by favoring particular paths and disregarding others. this manifests as low-level biasing which props up certain choices over others and increases the probability that they will be selected. biasing also decreases the probability that other choices will be considered before making a selection.

actions and paths

actions result from the selection of paths that eventually prompt motor responses. a successful plan is one that guides selection toward desired actions, which means influencing the decisions at branches so that the final point is the correct leaf. without the help of high-level plans, decisions are mostly random, and will eventually favor the most-travelled paths. this leads to getting stuck in local optima, which is where high-level plans come in. the goal of influencing lower levels is to push decision-making strategies toward a global optima.

a plan is generated when a particular path over time tends to result in a desired outcome. a newly formed path exerts a slight influence, and becomes stronger when it reliably leads to expected results. new plans are found by random behavior of low-level decisions, which lead to high-level coordination once a plan is sufficiently strengthened. the resulting influence overwhelmingly controls decision-making strategies at lower levels, increasing its strength even more. the strength of a plan is modeled as a power-law distribution, where the more successful a plan is over time, the more likely it is to be followed.