One of them, the RS Sarmat, was depicted as placing a nuclear weapon into a presumably orbital trajectory that could strike targets by traveling the long way around the globe in this case, with fictionalized land masses, but later depicted as descending on Florida. The US and USSR had already publicly stated their intention not to station nuclear weapons in space, on the Moon, or on other celestial bodies in through UN Resolutionbut because the statement was not legally binding, the US was not obligated to verify that the USSR was honoring the agreement. By contrast, in preparation for the legally binding Partial Nuclear Test Ban Treaty, which prohibited exo-atmospheric nuclear testing, the US deployed the Vela surveillance satellites to detect the characteristic radiation from exo-atmospheric and later, atmospheric nuclear explosions.
We can compose steps; but how do you compose score: Is it really environment reduction we're talking about here? I'm beginning to suspect that the performance score is a property of the agent, not the environment; this is consistent with the book's use of "reward" and "penalty.
On page 37, however, the authors state that: This notion of desirability [for a sequence of actions leading to a sequence of states] is captured by a performance measure that evaluates any given sequence of environment states.
I suspect that, whereas the environment is an arbiter of the performance score i. This is corroborated by the following: Notice that we said environment states, not agent states.
If we define success in terms of agent's opinion of its own performance, an agent could achieve perfect rationality simply by deluding itself that its performance was perfect. Since only the environment has access to its true states, it alone can measure performance.
Is this problematic in cases where we don't have an omniscient environment that directly communicates performance scores? In such cases, we'd have to rely on the imperfect self-judgement of the agent; and attempt to converge on rationality by internal coherence.
What I'm calling environments, incidentally, are now just functions: Agent combinators are a little tough, though; the performance measure has to be aware of the combined features.
Can we use some kind of message-passing mechanism? What stops us, for instance, as modelling the agents as lambdas; too? Part of the problem is the inversion of control: Every agent would be a dispatch-mechanism that would manage its own meta-variables including score and e.
Is it problematic, however, to have agents managing their own score? That way, agents only maintain state according to its percepts. Score, for instance, is not a percept in the vacuum world; location, however, is.
Agents, then, are functions with closures; functions which take as many parameters as their percepts have components.
Problem is, though, that we'd have to break the nice contract we have: To maintain the performance measure table, we'd have to receive the agent and the new score. How to make the performance measure part of the environment, so that we can relieve the agent from metadata? By maintaining agents-as-lambda, we get a certain flexibility; on the other hand, we shunt some complexity onto the environment: Is this an acceptable tradeoff?
The alternative, where I need to guess what agents need program, score, location seems onerous; for some reason. In practice, however, it may be simpler.
We haven't even solved the agent-hashing-problem, for instance wherein hashing fails if we mutate a field. Can we hash closures?
I see now why objects are interesting; closures, of course, do the same thing. Or, we can demand that the user furnish them; better yet, we can give the user the option of furnishing and ignoring them.
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Also, shouldn't we be able to name agents at some point? This would also have to fall within an external data structure. Maybe the record solution isn't problematic if we create ad-hoc agents for each problem. If we really need to decouple the program from the agent metadata do we?
This metadata table, on the other hand, would have to be passed to each subenvironment for composition. Seems like a pain.
We found that, since environments consist of a step function, we could reduce them to a lambda; let's see if this continues to be the case. For the time being, however, I think using agent-records is simplifying. I wouldn't mind agents being lambdas with closures; problem is: Message passing simulates records.
We could possibly do it with some kind of multiple-return-values hack, in which the subsequent values are ignored the agent effectively does a state dump every time its program is invoked.
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