In a projected future, I could imagine how to make the system be able to tell if “undesired story state” is happening, but it’s much less clear how the “take action to correct this state” is achieved… Did Jarmo yet suggest any ideas of how he plans to do that? That seems pretty hard to me.

Dragging the story from an undesirable state to a desirable one is undoubtedly a difficult task. So far I have been thinking about it on a high level, but the nitty-gritty details remain to be worked out. The way I have been thinking about it so far is that you have a set of ‘effectors’ that are applied to drag the story from an undesirable to a desirable state. An effector could for instance be “introduce a new topic of discussion”, “give agent a new higher level goal to pursue” or “have a temper tantrum”. Guidance about which effector(s) that should be applied in a certain situation is given by preconditions associated with transitions to desirable states. For example if a character is required to have a certain emotional state for a desirable transition to take place effectors that cause such an emotional change in the character are chosen. However, I am not sure about what level that effectors should work on yet. Maybe they should work on individual agent goals, plans and intentions but it might be more appropriate if they work by instead selecting appropriate ‘beats’ to perform a la Facade (or something completely different). The future will tell.

There is certainly a case to be made that this would decrease the consistency of the world and possibly generally be a worse experience, even if all AI problems were solved. Comments?

I’d just like to add my understanding of how you are currently thinking of selecting an effector.

In the case that the bad state is implicit (meaning that the finite state machine failed to accept the stream of story world changes), you search through the effectors looking for one that modifies the story world state so as to produce a stream of story world changes (a history) that the FSM can accept. Depending on the number of effectors and the complexity of abstract story world state you’re maintaining, this search could become expensive.

In the case that you’ve explicitly modeled the bad state in the FSM (a node marked as “bad”), then the transition from the bad state back to a good state provides information about which effector to use. Presumably there are many more bad than good story states, so you wouldn’t want to have to explictly model all of them in the FSM. By modeling a subset of them (and, through the transitions back to good states, providing effector hints), the author can control the combinatorial explosion of the search for an appropriate effector.

Is this understanding correct?

I don’t think that adding a dose of narrativity to games makes them _more_ compelling in general. It might even be the case that doing so makes the experience worse (although I am not entirely sure about what you mean by that). Many of the non-narrative games out there are great and I love playing them!

However, I do believe that games that already have a narrative structure, e.g. role-playing games or adventure games could benefit from an even stronger narrative structure, but also a more interactive structure. Players interaction with the game should influence the story more than it currently does.

In addition I believe that there is a need for alternatives to the action oriented games that are currently dominating the market. Games that focus on telling a story, have a slower pace and require less motor skills might be an alternative for players that are not in to action games for one reason or the other e.g. elderly.

Your understanding is correct. However, each effector will typically change one parameter/value and a state transition places conditions on many such values. My hope is that effectors that turns values in the right directon can be applied as soon as they are found. The longer the process can go on before it has to become reactive (i.e. the systems head start is eaten up) the more effectors can be applied. So in a sense it would be an any-time algorithm.

Your work sounds interesting, and it’s really nice that you can come here and continue the discussion.

I have always felt that abstracting charcter actions into story actions is an extremely difficult process. Often it seems you are creating some kind of model of the characters in your story system. Using the character achiticture to “Fast Forward” the simulation to get information is one solution that avoids this particular problem and seems promising, especially when you are searching for problems in the short term.

One quick question: is there one FSM per character and one for the story, or is there just one FSM that controls the character and story progression?

Meanwhile, there is a potential computational explosion, which brings up a question that has plagued my thinking for quite some time…

Can interactive story telling can be done considering just the immediate future (i.e., look ahead just a few steps and make corrections) or does it actually require considering the distant future?

I’ll leave out the technical details of this question for now, but I’m curious of people’s opinions.

Currently there is a single FSM that controls story progression through the characters. The characters themselves are coded in the JAM agent language. I’ll take this opportunity to say that I am not sure that a FSM is the best representation (for my purposes), but it is simple and good enough to start with. An alternative would be to use e.g. activation networks. One benefit of using networks is that it presumably would be easier to find the next state to aim for i.e, the most activated node.

As for whether interactive storytelling requires looking into the distant future I think it depends on how much control you want over the story. If your aim is to somehow “optimize” the story or the players experience of it, I think you need to look further ahead into the future. If on the other hand you are satisfied with the user having a good , but not necessarily optimal, experience I think you can get away with a shorter lookahead. Good but not necessarily optimal experiences are what I am aiming for in my work :-). I think this fits well with Robert Rosens definition of anticipatory systems (which I am very much influenced by) and his notion of how they may be controlled. Instead of constantly trying to find the best trajectory through a state space a correction mechanism kicks in only when the system predicts that something will go wrong. However I guess that the frequency with which this will happen depends on your definition of “wrongness”. I think I am just adding to the confusion.