| |
| public:t_720_atai:atai-18:lecture_notes_w7 [2018/10/05 08:52] – [Cumulative Learning] thorisson | public:t_720_atai:atai-18:lecture_notes_w7 [2024/04/29 13:33] (current) – external edit 127.0.0.1 |
|---|
| | What it is | The acquisition of information in order to improve performance with respect to some Goal or set of Goals. | | | What it is | The acquisition of information in order to improve performance with respect to some Goal or set of Goals. | |
| | Learning from experience | A method for learning. Also called "learning by doing": An Agent <m>A</m> does action <m>a</m> to phenomenon <m>p</m> in context <m>c</m> and uses the result to improve its ability to act on Goals involving <m>p</m>. All higher-level Earth-bound intelligences learn from experience. | | | Learning from experience | A method for learning. Also called "learning by doing": An Agent <m>A</m> does action <m>a</m> to phenomenon <m>p</m> in context <m>c</m> and uses the result to improve its ability to act on Goals involving <m>p</m>. All higher-level Earth-bound intelligences learn from experience. | |
| | Learning by obser on | A method for learning. An Agent <m>A</m> learns how to achieve Goal <m>G</m> by receiving realtime information about some other Agent <m>A'</m> achieving Goal <m>G</m> by doing action <m>a</m>. | | | Learning by observation | A method for learning. An Agent <m>A</m> learns how to achieve Goal <m>G</m> by receiving realtime information about some other Agent <m>A'</m> achieving Goal <m>G</m> by doing action <m>a</m>. | |
| | Learning from reasoning | A method for learning. Using deduction, induction and abduction to simulate, generalize, and infer, respectively, new information from acquired information. \\ Most effectively used in combination with Learning from Experience. | | | Learning from reasoning | A method for learning. Using deduction, induction and abduction to simulate, generalize, and infer, respectively, new information from acquired information. \\ Most effectively used in combination with Learning from Experience. | |
| | Multi-objective learning | Learning while aiming to achieve more than one Goal. | | | Multi-objective learning | Learning while aiming to achieve more than one Goal. | |
| | \\ The Learner | Intelligent systems continually receive inputs/observations from their environment and send outputs/actions back. Some of the system’s inputs may be treated specially — e.g. as feedback or a reward signal, possibly provided by a teacher. Since intelligent action can only be called "intelligent" if it is trying to achieve something - against which the level of intelligence can be evaluated - we model intelligent agents as imperfect optimizers of some (possibly unknown) real-valued objective function. | | | \\ The Learner | Intelligent systems continually receive inputs/observations from their environment and send outputs/actions back. Some of the system’s inputs may be treated specially — e.g. as feedback or a reward signal, possibly provided by a teacher. Since intelligent action can only be called "intelligent" if it is trying to achieve something - against which the level of intelligence can be evaluated - we model intelligent agents as imperfect optimizers of some (possibly unknown) real-valued objective function. | |
| | \\ Tasks | Learning systems adjust their knowledge as a result of interactions with a task- environment. Defined by (possibly a variety of) objective functions, as well as (possibly) instructions (i.e. knowledge provided at the start of the task, e.g. as a "seed", or continuously or intermittently throughout its duration). Since tasks can only be defined w.r.t. some environment, we often refer to the combination of a task and its environment as a single unit: the task-environment. | | | \\ Tasks | Learning systems adjust their knowledge as a result of interactions with a task- environment. Defined by (possibly a variety of) objective functions, as well as (possibly) instructions (i.e. knowledge provided at the start of the task, e.g. as a "seed", or continuously or intermittently throughout its duration). Since tasks can only be defined w.r.t. some environment, we often refer to the combination of a task and its environment as a single unit: the task-environment. | |
| | \\ Teacher | The goal of the teacher is to influence the learner’s task-environments in such a way that progress towards the is facilitated. The teacher’s teaching task is to change the learner’s knowledge in some way (e.g. to make the learner understand something, or increase the learner’s skill on some metric). | | | \\ Teacher | The goal of the teacher is to influence the learner’s task-environments in such a way that progress towards the learning goal is facilitated. The teacher’s teaching task is to change the learner’s knowledge in some way (e.g. to make the learner understand something, or increase the learner’s skill on some metric). | |
| | Environment & Task | The learner and the teacher each interact with their own view of the world (i.e. their own “environments”) which are typically different, but overlapping to some degree. | | | Environment & Task | The learner and the teacher each interact with their own view of the world (i.e. their own “environments”) which are typically different, but overlapping to some degree. | |
| | \\ Training | Viewed from a teacher’s and intentional learner’s point of view, “training” means the actions taken (repeatedly) over time with the goal of becoming better at some task, by avoiding learning erroneous skills/things and avoid forgetting or unlearning desirable skills/things. | | | \\ Training | Viewed from a teacher’s and intentional learner’s point of view, “training” means the actions taken (repeatedly) over time with the goal of becoming better at some task, by avoiding learning erroneous skills/things and avoid forgetting or unlearning desirable skills/things. | |
