public:t-713-mers:mers-25:task_theory
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public:t-713-mers:mers-25:task_theory [2025/08/26 10:48] – leonard | public:t-713-mers:mers-25:task_theory [2025/08/26 13:41] (current) – [Discussion Prompts] leonard | ||
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| **F**: Transformation Rules | Describe how variables evolve (physics, rules of a game, causal dynamics). These are **objective world relations**, | | **F**: Transformation Rules | Describe how variables evolve (physics, rules of a game, causal dynamics). These are **objective world relations**, | ||
| **C**: Constraints | | **C**: Constraints | ||
- | | | + | | Simple Task | Few variables, deterministic (press a button). | |
- | | | + | | Complex Task | Many variables, uncertainty, |
\\ | \\ | ||
==== Intricacy & Difficulty ==== | ==== Intricacy & Difficulty ==== | ||
- | | Intricacy (Observer) | + | | Intricacy (Observer) |
- | | Effective Intricacy (Agent) | + | | Effective Intricacy (Agent) |
+ | | Intricacy of Tasks | Based on (at least) three dimensions: | | ||
+ | | | The minimal number of causal-relational models needed to represent the relations of the causal structure related to the goal(s). | | ||
+ | | | The number, length and type of mechanisms of causal chains that affect observable variables on a causal path to at least one goal. | | ||
+ | | | The number of hidden confounders influencing causal structures related to the goal. | | ||
| Difficulty | | Difficulty | ||
| Example | | Example | ||
- | | Connection to ERS | Difficulty is the bridge between **objective task description** (for observers) and **empirical performance measures** (for agents). ERS requires both views: tasks must be defined *in the world* (observer) but evaluated *through agent behavior*. | | + | | Connection to ERS | Difficulty is the bridge between **objective task description** (for observers) and **empirical performance measures** (for agents). ERS requires both views: tasks must be defined |
+ | |||
+ | \\ | ||
+ | ==== Example of a Task with different Intricacy ==== | ||
+ | |||
+ | {{ : | ||
+ | Taken from [[https:// | ||
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| Periodicity | | Periodicity | ||
| Repeatability | | Repeatability | ||
+ | |||
+ | \\ | ||
+ | ==== Levels of Detail in Task Theory ==== | ||
+ | |||
+ | | What it is | Tasks can be described at different levels of detail — from coarse abstract goals to fine-grained physical variables. The chosen level shapes both evaluation (observer) and execution (agent). | | ||
+ | | Observer’s Perspective | ||
+ | | Agent’s Perspective | ||
+ | | Coarse Level | Only abstract goals and broad categories of variables are specified. Example: “Deliver package to location.” | | ||
+ | | Intermediate Level | Includes some measurable variables and causal relations. Example: “Move package from x to y using navigation map.” | | ||
+ | | Fine Level | Explicit representation of detailed physical dynamics, constraints, | ||
+ | | Implications for ERS | Enables systematic scaling of task complexity in experiments. \\ Supports fair comparison: two agents can be tested at the same or different levels of detail. \\ Clarifies where errors originate: poor reasoning vs. inadequate detail in task definition. | | ||
+ | |||
+ | \\ | ||
+ | |||
+ | ==== Intricacy and Level of Detail ==== | ||
+ | |||
+ | | Maximum Intricacy | ||
+ | | Problem | ||
+ | | Changing the task | If a task is too intricate to be performed, the task must be adjusted to fit the agent' | ||
+ | | Changing the Level of Detail | ||
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\\ | \\ | ||
- | ==== Discussion Prompts ==== | ||
- | | Question | ||
- | |------------|-----------------|--------------| | ||
- | | How is a " | ||
- | | Why must tasks be agent-independent? | ||
- | | Can you think of a task with low intricacy but high difficulty for humans? | ||
- | | What role does causality play in defining tasks? | ||
- | | How does a variable-task simulator (like SAGE) help ERS? | Observer: controls task parameters systematically | Agent: experiences wide range of tasks, supports empirical generality tests | |
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