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### #Actualwodinoneeye

Posted 16 August 2012 - 08:01 AM

"The idea behind utility-based systems is quite simple: Assign a score to each possible action in"

The old problem is coming up with a unified metric to "simply assign a score" (compared to THAT problem the rest is trivial)

Complex environments have too many endcases to have simple evaluation functions. Of course, the complexity of the decision logic increases exponentially with the complexity of the object's potential behaviors and its environment. (throw in handling uncertainty if you want it this be a magnitude harder and for temporally complex actions/results another magnitude).

Risk versus Reward (including Cost) analysis - how does the object's logic .judge a VECTOR of boiled down evaluation results adjusted for current preferences/historic success memories/changeable goal priorities --- to come up with a single value that it can competantly compare to another entirely different possibilities being 'considered'/evaluated?

You can try to find generalizations in the evaluation logic, but those endcases are legion.

Hand normalization (via cohesive judgement across the whole problem/solution space) . -- As usual the required human in the loop is the limitation.

A simple decision space you can probably comprehend and visualize it so as to tweak it into shape, but as it grows more complex it becomes a monster.

### #2wodinoneeye

Posted 16 August 2012 - 07:59 AM

"The idea behind utility-based systems is quite simple: Assign a score to each possible action in"

The old problem is coming up with a unified metric to "simply assign a score" (compared to THAT problem the rest is trivial)

Complex environments have too many endcases to have simple evaluation functions. Of course, the complexity of the decision logic increases exponentially with the complexity of the object's potential behaviors and its environment. (throw in handling uncertainty if you want it this be a magnitude harder and for temporally complex actions/results another magnitude).

Risk versus Reward (including Cost) analysis - how does the object's logic .judge a VECTOR of boiled down evaluation results adjusted for current preferences/historic success memories/changeable goal priorities --- to come up with a single value that it can competantly compare to another entirely different possibilities being 'considered'/evaluated?

You can try to find generalizations in the evaluation logic, but those endcases are legion.

Hand normalization (via cohesive judgement across the whole problem/solution space) .

As usual the required human in the loop is the limitation.

Simple decision space you can probably comprehend and visualize it so as to rweak it into shape, but as it grows more complex it becomes a monster.

### #1wodinoneeye

Posted 16 August 2012 - 07:58 AM

"The idea behind utility-based systems is quite simple: Assign a score to each possible action in"

The old problem is coming up with a unified metric to "simply assign a score" (compared to THAT problem the rest is trivial)

Complex environments have too many endcases to have simple evaluation functions. Of course, the complexity of the decision logic increases exponentially with the complexity of the object's potential behaviors and its environment. (throw in handling uncertainty if you want it this be a magnitude harder and for temporally complex actions another magnitude).

Risk versus Reward (including Cost) analysis - how does the object's logic .judge a VECTOR of boiled down evaluation results adjusted for current preferences/historic success memories/changeable goal priorities --- to come up with a single value that it can competantly compare to another entirely different possibilities being 'considered'/evaluated?

You can try to find generalizations in the evaluation logic, but those endcases are legion.

Hand normalization (via cohesive judgement across the whole problem/solution space) .

As usual the required human in the loop is the limitation.

Simple decision space you can probably comprehend and visualize it so as to rweak it into shape, but as it grows more complex it becomes a monster.

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