Ben
Yours is a sufficient response. Thank you.
Abram
Suppose we decompose a cognitive system down into a few components:
1. A planner (which is fed a goal, a current state and a set of possible
actions (i.e., operators, methods, cases, etc.)), 2. An action selector
(which is fed the current state, a prioritized set of goals, and a set of
methods to choose from), 3. A goal selector / Attention module whose job
is to prioritize or select goals for the cognitive system.
My question was what would you feed the goal selector to ensure it did its
job (prioritizing goals) properly?
In a paper I read recently "A Case Study of Goal-Driven Autonomy in
Domination Games" by Hector Munoz-Avila and David W. Aha the authors, in
their CB-gda system, decompose the cognitive system into two case-based
components (a) a planning component, and (b) a mismatch goal [selection]
component. The purpose of the latter component was to correct for errors
encountered by the planner. The input for the mismatch goal selection
component is a mismatch (the difference between the expected state and the
goal state).
Q: What else would be relevant input for a goal selector / Attention
component?
Date: Fri, 8 Jun 2012 17:49:15 -0400
Subject: Re: [agi] Attention
From: [email protected]
To: [email protected]
In the OpenCog framework, we supply some hard-coded "top level goals", and then
the system learns how to achieve these, which may include learning subgoals...
The top level goals are generally of the form "keep so-and-such parameter
within range [L,R]"
Experience of novelty and discovery of new things are good general top-level
goals. For an character in a virtual 3D environment, we add in stuff like
getting energy (e.g. from batteries or food), staying safe, and partaking in
social interaction....
In reference to this sort of framework, I'm unsure if you're talking about
top-level goals or learned subgoals...
-- Ben G
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AGI
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