While inductive inference is about finding models or hypotheses that _explain_ the data (whatever _explain_ actually shall mean), _prediction_ is concerned about forecasting the future. Finding models is interesting and useful, since they usually help us to (partially) answer such _predictive_ questions [12; 13]. While the usefulness of predictions is clearer to the layman than the purpose of the scientific inquiry for models, one may again ask, why we do or should we care about making predictions? **Decisions. ** Consider the following questions: Shall I take my umbrella or wear sunglasses today? Shall I invest my assets in stocks or bonds? Shall I skip work today because it might be my last day on earth? Shall I irradiate or remove the tumor of my patient? These questions ask for decisions that have some (minor to drastic) consequences. We usually want to make \"good\" decisions, where the quality is measured in terms of some reward (money, life expectancy) or loss [14; 15; 16]. In order to compute this reward as a function of our decision, we need to predict the environment: whether there will be rain or sunshine today, whether the market will go up or down, whether doomsday is tomorrow, or which type of cancer the patient has. Often forecasts are uncertain [17], but this is still better than no prediction. Once we arrived at a (hopefully good) decision, what do we do next? **Actions. ** The obvious thing is to execute the decision, i.e. to perform some action consistent with the decision arrived at. The action may not influence the environment, like taking umbrella versus sunglasses does not influence the future weather (ignoring the butterfly effect) or small stock trades. These settings are called passive [18], and the action part is of marginal importance and usually not discussed. On the other hand, a patient might die from a wrong treatment, or a chess player loses a figure and possibly the whole game by making one mistake. These settings are called (re)active [19], and their analysis is immensely more involved than the passive case [20]. **And now? ** There are many theories and algorithms and whole research fields and communities dealing with some aspects of induction, prediction, decision, or action. Some of them will be detailed below. Finding solutions for every particular (new) problem is possible and useful for many specific applications. Trouble is that this approach is cumbersome and prone to disagreement or contradiction [21]. Some researchers feel that this is the nature of their discipline and one can do little about it [22]. But in science (in particular math, physics, and computer science) previously separate approaches are constantly being unified towards more and more powerful theories and algorithms [23, 24]. There is at least one field, where we _must_ put everything (induction+prediction+decision+action) together in a completely formal (preferably elegant) way, namely Artificial Intelligence [25]. Such a general and formal theory of AI has been invented about a decade ago [26]. **Contents. ** In _Section 2_. I will give a brief introduction into this universal theory of AI. It is based on an unexpected unification of algorithmic information theory and sequential decision theory. The corresponding AIXI agent is the first sound, complete, general, rational agent in any relevant but unknown environment with reinforcement feedback [27, 28]. It is likely the best possible such agent in a sense to be explained below. _Section 3_. describes the historic origin of the AIXI model. One root is [PERSON]'s theory [29] of _universal induction_, which is closely connected to algorithmic complexity. The other root is [PERSON]'s _adaptive control theory_[30] for optimal _sequential decision_ making. Both theories are now half-a-century old. From an algorithmic information theory perspective, AIXI generalizes optimal passive universal induction to the case of active agents. From a decision-theoretic perspective, AIXI is a universal Bayes-optimal learning algorithm. _Sections 4.-7_.