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T-720-ATAI-2018

Lecture Notes, W3: Tasks & Environments

 Task A Problem that can be assigned. Typically comes with Instructions (guide to Solutions). Problem A Goal with (all) relevant constraints (≈ requirements). Problem Family A set of problems that are similar in some (important) ways; a Problem plus variations of that Problem. Goal A (future) State to be attained, plus optional constraints on the Goal. State A set of values (with error bounds) for a set of variables relevant to a Goal. Environment A set of constraints relevant to a Task but not counted as part of a Task proper. World A set of constraints that a set of Environments have in common. Constraint A set of factors that limit the flexibility of that which it constrains. Solution The set of (atomic) actions that can achieve a Goal. Action The changes an Agent can make to variables relevant to a Task-Environment. Plan A partial way to accomplish a Task. Instructions Partial Plan for accomplishing a Task, typically given to an Agent along with a Task by a Teacher. Teacher The Agent assigning a Task to another Agent (student), optionally in charge of Instructions.

 Solution Constraint Reduces the flexibility for producing a Solution. Task Constraint Limits the allowed Solution Space for a Problem. Can help or hinder a Task to be achieved. Solution Space The amount of variation allowed on a State while still counting as a Solution to a Problem. Task Space The size of variations on a Task that would have be explored with no up-front knowledge or information about the Solution Space of a Problem.

Limited Time & Energy

 Task All tasks have a limited time & energy: No Task exists that can be performed with infinite energy, or for for which infinite time is available for achieving. LTE Limited Time & Energy.

 Closed Problem May be assigned as a Task with known Time & Energy for achieving a solution. Example Doing the dishes. Plans for Closed problems Can be reliably produced for closed problems, e.g. in the form of Instructions. Open Problem A Problem whose solution is unknown and cannot be obviously assumed from analogy with similar problems whose solution is known. Cannot be guaranteed a solution with LTE. Example Any research problem for which no known solution exists. Plans for Open problems Cannot be reliably produced.

What Kind of Worlds?

 Balance The Worlds we are interested in strike a balance between completely dynamic and completely static. They also strike a balance between completely deterministic and completely random; some regularity must exist at a level that is observable initially by a learning agent. Completely static No (or little) need for learning. Completely random Learning is of no use. Limited time No Task, no matter how small, takes zero time. “Any task worth doing takes time.” All implemented intelligences will be subject to the laws of physics. Limited energy “Any task worth doing takes energy.” All implemented intelligences will be subject to the laws of physics. No task takes zero time or energy If is a function that returns time and energy, an act of perception a decision in , and an action . It follows deductively that since any Task requires at the very least one of each of these, in the minimum case to decide whether the Goal of a Task is achieved, then . Anything that takes zero time or zero energy is by definition not a Task.

 Environment Large number of potentially relevant variables. Task Ditto. Medium number of Solutions. Instructions: possibly.

How it Hangs Together: Worlds, Environments, Tasks, Goals

 World A set of variables with constraints and relationships. where is a set of variables and is a set of transition functions / rules describing how the variables can change. Static World Changes State only through Agent Action. Dynamic World Changes State through Agent Action and through other means. Physical World In a physical world are real-valued variables, and represent the first derivative of the variables during continuous change. State A set of values (with constraints, e.g. error bounds) for a set of variables relevant to a World. For all practical purposes, in any complex World we will speak of “State” even for sub-states, as most useful States will be sub-states, since there will always be a vastly higher number of “don't care” variables than the variables listed for e.g. a Goal State. State definition where define lower and upper bounds on acceptable range for each to count towards the State, respectively. Environment where are additional constraints on and some values for are fixed. Task A Problem that can be assigned in an Environment. Typically comes with Instructions (guide to Solutions, partial Solution or full Solution - at some level of maximum detail). “Task” definition An assigned Problem. Task-Environment An Environment in which one or more Tasks may be assigned. Problem A Goal with (all) relevant constraints imposed by a Task-Environment. Goal A (future) (sub-) State to be attained during period t, plus other optional constraints on the Goal. “Goal” definition attached to a Problem.

 Family A set whose members share one or more common trait within some sensible (defined) allowed variability, defined as one or more of the types of variables, number of variables, the ranges of these variables. Problem Family A set of problems that are similar in important ways; a Problem and its variations. Domain A Family of Environments. Constraint A set of factors that limit the flexibility of that which it constrains. Solution The set of (atomic) actions that can achieve a Goal in a Task-Environment. Action The changes an Agent can make to variables relevant to a Task-Environment. Plan A partial way to accomplish a Task. Instructions Partial Plan for accomplishing a Task typically given to an Agent by a Teacher, along with a Task. Teacher The Agent assigning a Task to another Agent (student).

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