Welcome to alt.friam.flames! (You geezers know what I'm talking about.) ;-}
FWIW, I was thinking along similar lines, i.e. that a hybrid ABM / rule-based simulation framework would be amenable to implementation in C++. I was also going to make an observation that people (here on this list, as well as elsewhere) seem to constantly be in search of some kind of 'one size fits all' simulation development environment, and that such always fail because they are too heavy-weight, too cumbersome, and too constraining. But I won't make that comment now. --Doug On Mon, Aug 24, 2009 at 9:43 AM, Owen Densmore <[email protected]> wrote: > Whew, good thing I didn't make the NetLogo response, its exactly what I was > thinking of though. It would be quite easily done, I believe, by a > competent NetLogo programmer. > > We have several here on the list .. they could let us know if Eric and I > are wrong. > > Russ, there is no reason to be so rude. It makes you appear a pouting ass. > > -- Owen > > > > On Aug 23, 2009, at 10:37 PM, Russ Abbott wrote: > > Eric, >> >> You said, "Not knowing what you want to do ...". >> >> It's clear from the rest of your message that you're absolutely right. You >> have no idea what I want to do. >> >> What amazes me is that you nevertheless seem to think that you can tell me >> the best way for me to do it. How can you be so arrogant? >> >> Perhaps that's also what went wrong in our discussion of consciousness a >> while ago. >> >> -- Russ >> >> >> >> On Mon, Aug 24, 2009 at 1:58 PM, ERIC P. CHARLES <[email protected]> wrote: >> Russ (and everyone else), >> Just because its what I know, I would do it in NetLogo. I'm not suggesting >> that NetLogo will do what you want, just that it can simulate doing what you >> want. Not knowing what you want to do, lets keep it general: >> >> You start by making an "agent" with a list of things it can do, lets label >> them 1-1000, and a list of things it can sense, lets label them A-ZZZ. But >> there is a catch, the agent has no commands connecting the sensory list to >> the behaviors list, a different object must do that. The agent must query >> all the rules until it finds one that accepts its current input, and then >> the rule sends it a behavior code. (Note, that any combination of inputs can >> be represented as a single signal or as several separate ones, it doesn't >> matter for theses purposes) >> >> You then make several "rules", each of which receives a signal from an >> agent and outputs a behavior command. One rule might be "If input WFB, then >> behavior 134." Note, it doesn't matter how complicated the rule is, this >> general formula will still work. Any countable infinity of options can be >> re-presented using the natural numbers, so it is a useful simplification. >> Alternatively, imagine that each digit provides independent information and >> make the strings as long as you wish. >> >> Now, to implement one of my suggestions you could use: >> 1) The "system level" solution: On an iterative basis asses the benefit >> gained by individuals who accessed a given rule (i.e. turtles who accessed >> rule 4 gained 140 points on average, while turtles who accessed rule 5 only >> gained 2 points on average). This master assessor then removes or modifies >> rules that aren't up to snuff. >> >> 2) The "rule modified by agents" solution: Agents could have a third set >> of attributes, in addition to behaviors and sensations they might have "rule >> changers". Let's label them from ! to ^%*. For example, command $% could >> tell the rule to select another behavior at random, while command *# could >> tell the rule to simply add 1 to the current behavior. >> >> 3) The "agents disobey" solution: Agents could in the presence of certain >> sensations modify their reactions to the behavior a given rule calls up in a >> permanent manner. This would require an attribute that kept track of which >> rules had been previously followed and what the agent had decided from that >> experience. For example, a given sensation may indicate that doing certain >> behaviors is impossible or unwise (you can't walk through a wall, you don't >> want to walk over a cliff); under these circumstance, if a rule said "go >> forward" the agent could permanently decide that if rule 89 ever says "go >> forward" I'm gonna "turn right" instead.... where "go forward" = "54" and >> "turn right" = "834". In this case the object labeled "rule" is still the >> same, but only because the effect of the rule has been altered within the >> agent, which for metaphorical purposes should be sufficient. >> >> Because of the countable-infinity thing, I'm not sure what kinds of thing >> a system like this couldn't simulate. Any combination of inputs and outputs >> that a rule might give can be simulated in this way. If you want to have 200 >> "sensory channels" and 200 "limbs" that can do the various behaviors in the >> most subtle ways imaginable, it would still work in essentially the same >> way, or could be simulated in exactly the same way. >> >> Other complications are easy to incorporate: For example, you could have a >> rule that responded to a large set of inputs, and have those inputs >> change... or you could have rules link themselves together to change >> simultaneously... or you could have the agent send several inputs to the >> same rule by making it less accurate in detection. You could have rules that >> delay sending the behavior command... or you could just have a delay built >> into certain behavior commands. >> >> >> Eric >> >> P.S. I'm sorry for the bandwidth all, but I am continuing to communicate >> through the list because I am hoping someone far more experienced than I >> will chime in if I am giving poor advice. >> >> >> >> >> On Sun, Aug 23, 2009 10:32 PM, Russ Abbott <[email protected]> wrote: >> My original request was for an ABM system in which rules were first class >> objects and could be constructed and modified dynamically. Although your >> discussion casually suggests that rules can be treated the same way as >> agents, you haven't mentioned a system in which that was the case. Which >> system would you use to implement your example? How, for example, can a rule >> alter itself over time? I'm not talking about systems in which a rule >> modifies a field in a fixed template. I'm talking about modifications that >> are more flexible. >> >> Certainly there are many examples in which rule modifications occur within >> very limited domains. The various Prisoner Dilemma systems in which the >> rules combine with each other come to mind. But the domain of PD rules is >> very limited. >> >> Suppose you really wanted to do something along the lines that your >> example suggests. What sort of ABM system would you use? How could a rule >> "randomly (or non-randomly) generate a new contingency" in some way other >> than simply plugging new values into a fixed template? As I've said, that's >> not what I want to do. >> >> If you know of an ABM system that has a built-in Genetic Programming >> capability for generating rules, that would be a good start. Do you know of >> any such system? >> >> -- Russ >> >> >> >> >> On Mon, Aug 24, 2009 at 11:10 AM, ERIC P. CHARLES <[email protected]> wrote: >> Well, there are some ways of playing fast and loose with the metaphor. >> There are almost always easy, but computationally non-elegant, ways to >> simulate things like this. Remember, we have quotes because "rules" and >> "agents" are just two classes of agents with different structures. >> >> Some options: >> 1) The "rules" can alter themselves over time, as they can be agents in a >> Darwinian algorithm or any other source of system level change you want to >> impose. >> 2) The "rules" could accept instructions from the "agents" telling them >> how to change. >> 3) The "agents" could adjust their responses to commands given by the >> "rules" which effectively changes what the rule (now not in quotes) does. >> >> To get some examples, let's start with a "rule" that says "when in a red >> patch, turn left". That is, in the starting conditions the "agent" tells the >> rule it is in a red patch, the "rule" replies back "turn left": >> 1) Over time that particular "rule" could be deemed not-useful and >> therefore done away with in some master way. It could either be replaced by >> a different "rule", or there could just no longer be a "rule" about what to >> do in red patches. >> 2) An "agent" in a red patch could for some reason no longer be able to >> turn left. When this happens, it could send a command to the "rule" telling >> the "rule" it needs to change, and the "rule" could randomly (or >> non-randomly) generate a new contingency. >> 3) In the same situation, an "agent" could simply modify itself to turns >> right instead; that is, when the command "turn left" is received through >> that "rule" (or perhaps from any "rule"), the "agent" now turns right. This >> is analogous to what happens at some point for children when "don't touch >> that" becomes "touch that". The parents persist in issuing the same command, >> but the rule (now not in quotes) has clearly changed. >> >> Either way, if you are trying to answer a question, I think it something >> like one of the above options is bound to work. If there is some higher >> reason you are trying to do something in a particular way, or you have >> reason to be worried about processor time, then it might not be exactly what >> you are after. >> >> Eric >> >> >> >> On Sun, Aug 23, 2009 05:18 PM, Russ Abbott <[email protected]> wrote: >> Thanks Eric. It doesn't sound like your suggestion will do what I want. I >> want to be able to create new rules dynamically as in rule evolution. As I >> understand your scheme, the set of rule-agents is fixed in advance. >> >> -- Russ >> >> >> >> On Sun, Aug 23, 2009 at 8:30 AM, ERIC P. CHARLES <[email protected]> wrote: >> Russ, >> I'm probably just saying this out of ignorance, but... If you want to >> "really" do that, I'm not sure how to do so.... However, given that you are >> simulating anyway... If you want to simulate doing that, it seems >> straightforward. Pick any agent-based simulation program, create two classes >> of agents, call one class "rules" and the others "agents". Let individuals >> in the "rules" class do all sorts of things to individuals in the "agents" >> class (including controlling which other "rules" they accept commands from >> and how they respond to those commands). >> >> Not the most elegant solution in the world, but it would likely be able to >> answer whatever question you want to answer (assuming it is a question >> answering task you wish to engage in), with minimum time spent banging your >> head against the wall programming it. My biases (and lack of programming >> brilliance) typically lead me to find the simplest way to simulate what I >> want, even if that means the computers need to run a little longer. I assume >> there is some reason this would not be satisfactory? >> >> Eric >> >> >> >> >> On Sat, Aug 22, 2009 11:13 PM, Russ Abbott <[email protected]> wrote: >> Hi, >> >> I'm interesting in developing a model that uses rule-driven agents. I >> would like the agent rules to be condition-action rules, i.e., similar to >> the sorts of rules one finds in forward chaining blackboard systems. In >> addition, I would like both the agents and the rules themselves to be first >> class objects. In other words, the rules should be able: >> >> • to refer to agents, >> • to create and destroy agents, >> • to create new rules for newly created agents, >> • to disable rules for existing agents, and >> • to modify existing rules for existing agents. >> Does anyone know of a system like that? >> >> -- Russ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> lectures, archives, unsubscribe, maps at http://www.friam.org >> Eric Charles >> >> Professional Student and >> Assistant Professor of Psychology >> Penn State University >> Altoona, PA 16601 >> >> >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> lectures, archives, unsubscribe, maps at http://www.friam.org >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> lectures, archives, unsubscribe, maps at http://www.friam.org >> Eric Charles >> >> Professional Student and >> Assistant Professor of Psychology >> Penn State University >> Altoona, PA 16601 >> >> >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> lectures, archives, unsubscribe, maps at http://www.friam.org >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> lectures, archives, unsubscribe, maps at http://www.friam.org >> Eric Charles >> >> Professional Student and >> Assistant Professor of Psychology >> Penn State University >> Altoona, PA 16601 >> >> >> >> ============================================================ >> FRIAM Applied Complexity Group listserv >> Meets Fridays 9a-11:30 at cafe at St. John's College >> lectures, archives, unsubscribe, maps at http://www.friam.org >> >>
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