Difference between revisions of "Comparison of terms related to agency"
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* online vs offline (maybe also one-shot vs continually outputting things) | * online vs offline (maybe also one-shot vs continually outputting things) | ||
* how well does it continue working when the environment/input changes? | * how well does it continue working when the environment/input changes? | ||
− | * cross-domain optimization power | + | * [[general intelligence]] / cross-domain optimization power |
* coherence (if it makes sense to assign preferences) | * coherence (if it makes sense to assign preferences) | ||
* does it pursue omohundro's "basic AI drives"? (i.e. it is the subject of instrumental convergence) | * does it pursue omohundro's "basic AI drives"? (i.e. it is the subject of instrumental convergence) |
Revision as of 22:39, 1 March 2020
Term | Opposite |
---|---|
Agent | |
Optimizer, optimization process | |
consequentialist | |
expected utility maximizer | |
goal-directed, goal-based | act-based? |
pseudoconsequentialist | |
mesa-optimizer |
parameters to check for:
- is it searching through a list of potential answers?
- does it have an explicit model of the world? i.e. it has counterfactuals (see drescher on subactivation)
- can it be modeled as having a utility function?
- can it be modeled as having a utility function and selecting an action that does an argmax over actions? (note: this does not say how the argmax is implemented, which might be via a search or via some other more "control"-like process)
- can we take an intentional stance toward it? i.e., is it useful (so far as predicting what it will do is concerned) to model it as having intentions?
- is it solving some sort of optimization problem? (but what counts as an optimization problem?)
- origin: was it itself produced by some sort of optimization process?
- eliezer's GLUT idea of "trace the improbability"
- does it hit a small target, out of a large space of possibilities?
- how many elements in the space of possibilities is it instantiating?
- cost of evaluation of options/how good of a feedback we get for potential outputs
- online vs offline (maybe also one-shot vs continually outputting things)
- how well does it continue working when the environment/input changes?
- general intelligence / cross-domain optimization power
- coherence (if it makes sense to assign preferences)
- does it pursue omohundro's "basic AI drives"? (i.e. it is the subject of instrumental convergence)
- this post lists more formalizations for what "goal-directed" means
examples to check against:
- humans
- "subagents" inside of humans
- human who is modeled inside the mind of another human (e.g. for the purposes of predicting behavior)
- evolution/natural selection
- bottlecap
- RL system playing Pong without an explicit model
- tool AGI/CAIS
- task AGI
- KANSI
- mindblind AI
- targeting system on a rocket
- single-step filter
- chess-playing algorithm that just does tree search (e.g. alpha-beta pruning algorithm)
- a simple feed-forward neural network (e.g. one that recognizes MNIST digits)
- a thermostat
- a plant
- multi-armed bandit problem
- Solomonoff induction (outer layer/top-level reasoning)
- plagiarizing robot
- a system trained using imitation learning to behave similarly to another agent [1]
- an agent that always twitches; see jessica's comment here
See also
- List of terms used to describe the intelligence of an agent
- Coherence and goal-directed agency discussion
References
https://www.lesswrong.com/posts/ZDZmopKquzHYPRNxq/selection-vs-control
https://www.lesswrong.com/posts/rvxcSc6wdcCfaX6GZ/two-senses-of-optimizer
https://www.lesswrong.com/posts/Q4hLMDrFd8fbteeZ8/measuring-optimization-power