This working note discusses the topic of story generation, with a view to identifying the knowledge required to understand aviation incident narratives (which have structural similarities to stories), following the premise that to understand aviation incidents, one should at least be able to generate examples of them. We give a brief overview of aviation incidents and their relation to stories, and then describe two of our earlier attempts (using ‘scripts’ and ‘story grammars’) at incident generation which did not evolve promisingly. Following this, we describe a simple incident generator which did work (at a ‘toy’ level), using a ‘world simulation’ approach. This generator is based on Meehan’s TALE-SPIN story generator [Meehan, 1977]. We conclude with a critique of the approach.
Aviation incident reports describe and analyse unusual/unexpected chains of events during aircraft operation (eg. mechanical problems, unusual maneuvers). The part of the report describing the incident itself is typically an episodic narrative, and has similarities in structure to stories
, already studied in the linguistics literature. As part of a speculative exploration of achieving computer-based understanding of aviation incidents (that is, having a computer be able to answer various questions about incidents, including facts not explicitly stated in the narrative), we discuss and illustrate automatic generation of ‘toy’ aviation-incident-like reports. The premise here is that, if a computer is to be able to understand aviation incidents, it should at least be able to generate plausible incident ‘stories’. This requirement is quite a strong one, but is imposed here to ensure that key information about aviation incidents is captured; or, viewed another way, this requirement provides a good guide for delineating what a knowledge base about aviation safety should contain. It is also imposed to ‘keep the knowledge engineer honest’: ‘understanding-only’ systems can sometimes give the impression of understanding more than they really do111
For example, anecdotally one of the rules used for interpreting terrorism news articles in the Message Understanding Conference (MUC) Competitions was: “if you see an integer which is a multiple of ten, then this is probably the number of victims in the terrorist incident.”.
Some examples of very brief aviation incident summaries are given
below, from the FAA’s Incident Data Systsem (FIDES) publically available
961222042819C INOPERATIVE TRANSPONDER ON CLIMBOUT FROM AIRPORT. RETURNED. PUT IT ON MEL. CONTINUED FLIGHT. 961211044319C PASSENGER CUSSED OUT FLIGHT ATTENDANT TAXIING TO RUNWAY. PIC RETURNED TO GATE. PASSENGER REMOVED. 961216043479C TURBINE RIGHT ENGINE FAILED. DIVERTED TO RIC. OVERWEIGHT LANDING. HAD CONTAINED TURBINE FAILURE. 960712045359C PIC BECAME INCAPACITATED AFTER LANDING. LANDING WAS ERRATIC AS WAS TAXIING. FIRST OFFICER TOOK OVER. PIC HAD STROKE
In fact, aviation incidents are not quite the same as ‘stories’: although they contain plausible sequences of events, the sequences are non-fictional, and have not been crafted to deliberately interesting or suspenseful (though of course many are); similarly the narrative is intended to be clear and factual, rather than written with suspense and intrigue. These differences are important when considering the relevance of story generation systems to incident generation.
Early work on story generation systems (most notably Meehan’s TALE-SPIN [Meehan, 1977]) treated story generation as synonymous with world simulation, where the world contained (possibly several) agents who rationally tried to achieve their goals. This task in itself is an interesting and challenging one, and characterizes well what an aviation incident generation system should do. Later work, eg. [Dehn, 1981], pointed out that story generation is more than just simulation: the author him/herself has goals (eg. create an interesting story), and will explore multiple ways of unfolding a story to best achieve those goals. A story generator thus involves not only world simulation, but also meta-level control manipulating both the way that simulation plays out, and the characters/places/world being simulated. In addition, as Smith and Witten point out [Smith and Witten, 1991], there is an important distinction between the story itself and the telling of it (eg. events may be related in non-chronological order), a distinction which almost all computer-based story generators fail to make. However, for our goals of aviation incident generation, a simulation-only approach is a good starting point, and one which we focus on in this paper.
2 Approaches to Episodic Narrative Generation
Before describing this ‘simulation’ approach, we first describe two other approaches which we explored, both based on using pre-defined fragments of event sequences which might occur. Neither of these seemed to be evolving into a practical system, so they were abandoned. However, it is worth briefly mentioning them. It is also worth noting that the ‘scripts’ and ‘story grammars’ were originally proposed in the literature for story understanding, not generation, so our difficulties do not necessarily reflect a problem with these ideas, but with an attempt to make them do something that they were originally not designed for (but might plausibly have been useful for). In Section 5.1 we return to offer further reflections on story grammars with hindsight.
2.1 A Script-based Approach
In a script-based approach, a library of scripts is manually created, describing different abstract sequences of events appropriate to aviation incidents. To generate an incident, the computer selects and appropriately interleaves a suitable set of scripts. For example, consider the component scripts, and their interleaving to create an incident, shown in Figure 1.
Several challenges became apparent with this approach. First, there are a lot of scripts, including many simple variants of the same general theme. For example, the conceptual idea of ‘aborted flight’ has many associated scripts (“loadunload”, “loadtaxi-outtaxi-outtaxi-backunload”, “loadtaxi-outtakeoffreturntaxi-backunload”, …). Second, there are many causal interactions and constraints between scripts, making the proper and automatic interleaving of scripts extremely complicated (eg. the plane should be diverted after a problem has occurred, not before, and only if it’s a serious problem). Similarly, the choice of which scripts are appropriate and combinable is difficult to make. Finally, the scripts contain no representation of why an event occurs (eg. the plane returns to the start airport because it is important to land as soon as possible, where it is safe), making this approach intuitively less appealing.
2.2 A Story Grammar Approach
Several authors have proposed the use of ‘story grammars’ to capture the structure of stories (eg. [Rumelhart, 1975], and see [Wilensky, 1983] for a discussion). For example, an ‘aviation incident’ grammar might be as follows, using Definite Clause Grammer notation:
incident --> start-of-flight, problem(P), response(P). start-of-flight --> [taxi]. start-of-flight --> [taxi,takeoff]. start-of-flight --> [taxi,takeoff,cruise]. problem(broken(P)) --> broken(P). problem(bad_weather(W)) --> bad_weather(W). broken(transponder) --> [transponder_broke]. bad_weather(stormy) --> [stormy]. response(broken(transponder)) --> return_to_ground. ...
Again, this approach presents some challenges. First, as with
scripts, there are often many situation-specific ways of realizing
a single conceptual action. For example, the notion of
return_to_ground above may involve aborted takeoff,
divert to nearest airport, continue with landing, or no action at
all (if the airplane is already on the ground). The grammar starts
to look messy when attempting to account for these different options.
Second, an incident often involves interleaved events, for example:
The plane took off, then a passenger was ill, then bad weather hit, …
Stories (at least aviation incidents) appear to be less constrained
and more multithreaded than the structure of an English sentence,
making grammars less suitable for modeling their structure.
3 A World Simulation Approach
We now describe a simple incident generation system, which is essentially a simplified reconstruction of Meehan’s TALE-SPIN system [Meehan, 1977]. It also has some interesting similarities to Smith and Witten’s TAILOR system [Smith and Witten, 1991], which we summarize later in Section 5.2.
TALE-SPIN generates simple fables, using a world simulator in which events unfold. Rather than being based on pre-defined fragments of sequences, rational sequences of actions are created on-the-fly using a backward-chaining planning algorithm. An example story from TALE-SPIN is:
“Once upon a time, there was a dishonest fox named Henry who lived in a cave, and a vain and trusting crow named Joe who lived in an elm tree. Joe had gotten a piece of cheese and was holding it in his mouth. One day, Henry walked from his cave, across the medow to the elm tree. He saw Joe Crow and the cheese and became hungry. He decided that he might get the cheese if Joe Crow spoke, so he told Joe that he liked his singing very much and wanted to hear him sing. Joe was very pleased with Henry and began to sing. The cheese fell out of his mouth, down to the ground. Henry picked up the cheese and told Joe Crow that he was stupid. Joe was angry, and didn’t trust Henry anymore. Henry returned to his cave.” [Meehan, 1977, p97]
In some ways, TALE-SPIN can be viewed as a generalization of a STRIPS-like planner222ie. backward-chaining from a goal, using a precondition/effects action representation, although TALE-SPIN’s actual implementation appears cruder than STRIPS (eg. does it maintain a “protected goal” list?) [Fikes et al., 1981]. The original STRIPS planner produced a sequence of actions to achieve a pre-defined goal, that is it produced a ‘rational’ plan. However, it operated in an inert world – the actions were deterministic and guaranteed to work, no surprises or problems occured (eg. the tower falls down when stacking blocks). In contrast, TALE-SPIN’s world is more dynamic: there are multiple characters pursuing different goals, and (presumably) non-volitional ‘happenings’ can also occur in the world, requiring the characters to react.
Our ‘reconstruction’ is simple and highly improvised, based only on the general ideas in Meehan’s 1977 paper (rather than a detailed reimplementation of his system). The reconstruction undoubtedly misses out a lot of Meehan’s program, and also adds in new parts and approaches that Meehan didn’t originally conceive of or emphasize. The source Prolog code for the ‘reconstruction’ is given in the Appendix (written from scratch for this exercise). The incidents generated are fictitious, toy examples – the goal in this brief exercise was to investigate the structure of a generation system, rather than aim for detailed fidelity to true incidents. The reader should be aware that the simplicity of these toy incidents is in stark contrast to the complexity of real aviation incidents.
In our reconstruction, an incident is represented as a sequence of events, which transforms an initial situation to a final situation. A situation is represented as a set of facts, for example the start situation in all the incidents generated is:
||% the passengers are at the Seattle gate|
||% the airplane is at the Seattle gate|
||% can fly from Seattle to Chicago|
||% can fly from Chicago to Dallas|
||% airplane1 is an airplane|
||% passengers1 is a group of passengers|
An event is represented in the usual STRIPS fashion, with precondition facts, an add-list of facts which become true after the event has occured, and a delete-list of facts which become false. For example, taxiing to the runway is represented:
||% To take off…|
||% must be on the runway…|
||% result: no longer on runway..|
||% ..but (just) near the airport|
An event is either an action, which the pilot can perform (eg. take_off, above), or a happening, which is out of his/her control (eg. ill_passenger, meaning a passenger becomes ill). A STRIPS-like planner forms one component, and is used to generate a plan (action sequence) for the airplane pilot to achieve his/her goal (initially, to get the passengers to Dallas, ie. to achieve plocation(passengers1, gate(dallas))). There is also an independent ‘simulator’ component which simulates execution of that plan. Given a plan, the simulator will execute it one step at a time. However, it may also (with some probability) make a ‘happening’ occur during the execution, causing the current situation to change unexpectedly. When a happening occurs, the original goal (of the pilot) is reassessed – for example, the original goal of ‘get the passengers to Dallas’ (plocation(passengers1, gate(dallas))) may change to ‘get the passengers to safety as quickly as possible’ (p_on_ground(passengers1)). If the goal changes, then the pilot’s current plan is abandoned, and a new plan is generated on the fly to achieve the new goal from the current situation. The simulator then continues with the simulation, executing the new plan. In this implementation, just one happening is introduced in an incident, but in principle several could occur. The incident ends when the pilot achieves his/her (current) goal. English text is generated from the incident representation using simple fill-in-the-blank text templates, and was not a focus of this short project. An example (annotated) incident generated is the following:
Initially, the pilot goal = get the passengers to Dallas. Hence initial plan = taxi take-off cruise to Dallas land taxi to gate. The simulator starts executing the plan…
The passengers boarded the plane.
The plane taxiied to the runway.
The plane took off from seattle.
Now a happening is introduced…
A passenger became very ill.
The pilot revises his/her goal, to be get medical help for the passenger. Hence new plan = land taxi to gate get medical help.
The plane landed at seattle.
The plane taxiied to the gate.
The passengers disembarked.
Medical help was provided.
Generation of an incident thus involves the rational pursuit of goals, combined with the introduction of unexpected events (‘happenings’) and rational modification of goals in the light of the new facts. Some other example incidents generated by the system are shown below:
The passengers boarded the plane. The plane taxiied to the runway. The plane took off from seattle The plane cruised towards chicago The plane cruised towards dallas The engine caught fire. The plane landed at dallas The passengers were evacuated from the plane. ---------- The passengers boarded the plane. A passenger became very ill. The passengers disembarked. Medical help was provided. ---------- The passengers boarded the plane. The plane taxiied to the runway. The plane took off from seattle The plane cruised towards chicago The engine caught fire. The plane landed at chicago The passengers were evacuated from the plane.
The knowledge required to generate these is as follows:
A STRIPS-like representation of the actions the pilot can perform (takeoff, land, etc.). These describe the preconditions for the action, the facts which become false as a result of performing the action, and the facts which become true.
A similar STRIPS-like representation of ‘happenings’ which can occur.
A representation of how the pilot goals should change should something unexpected occur (eg. IF ill patient THEN new goal is to get medical help).
A scoring function, for ranking different plans and choosing the best.
This last item is particularly interesting – a ‘rational’ plan is not only one which achieves the goal, but one which is of minimum cost. For example, the following ‘irrational’ incidents were generated during debugging the system, when plans were not ranked according to their quality:
The passengers boarded the plane. The plane taxiied to the runway. The engine caught fire. The plane took off from seattle. The plane cruised towards chicago. The plane cruised towards dallas. The pilot made an emergency landing near somewhere. The passengers were evacuated from the plane.
In this case, the engine fire causes a new goal of ‘get the plane to the ground’. However, the plan of taking off and landing again is considered just as effective as staying put, and (here) is carried out.
Similarly in this buggy incident:
The passengers boarded the plane. The plane taxiied to the runway. The plane took off from seattle The plane cruised towards chicago. The plane cruised towards dallas. The plane landed at dallas. The passengers were evacuated from the plane.
evacuating the plane (even though there is no problem) is considered just as good a way of getting the pasengers to Dallas as delivering them right to the gate.
This simple TALE-SPIN-like system is able to generate plausible ‘toy’ aviation incidents; part of the reason it is able to do this is that the underlying representation captures the relationships between goals, plans, actions, and happenings. It could thus, in principle, answer questions such as “Why did the pilot do X?”, or “Why did X happen?”. Representing these relationships seems essential to the computer having some reasonably deep model of what is happening during an incident.
There are several simplifications in this small implementation which are worth noting. None seem to be fundamental obstacles to making the generator richer, and we speculate on how the system could be generalized:
Deterministic actions. In the implementation, actions are deterministic, ie. their outcomes are certain. To account for non-deterministic actions:
the STRIPS-like action representation would have to be generalized to represent the possible outcomes (eg. as different sets of add/delete lists), and their associated probabilities.
the planner would need to to explore possible branches of a plan’s execution, and their probability of occurence. The overall ‘quality’ of a plan would be some function of its possible outcomes and their associated probability.
the simulator would similarly need to randomly select one possible outcome of an action, again based on the outcomes’ associated probabilities.
Linear plans. In this implementation, a plan is a sequence of actions. However, a more sophisticated approach would allow for conditional plans (eg. “do X, then if Y happens do Z else do Z”), conceptually more like a flow-chart than a list of actions. This would make the planner more complex, but would allow it to account for different potential outcomes of an action, and even anticipate possible happenings occuring and plan corrective actions for them ahead of time333This forms an important part of (real) pilot training, where, for example, pilots should be constantly aware of where they plan to land if there is an emergency.. Here, the planning algorithm starts to ressemble game-playing algorithms, where a branching tree of actions and possible reactions is searched to decide the best way to proceed. If we assume the pilot has a good model of the world, the planning algorithm should use the simulation algorithm to develop a plan (in the current implementation, the planning algorithm uses a different and simpler ‘simulation’ engine, in which happenings do not occur).
Multiple agents. In the system, there is just one agent (the pilot), interacting with a world. A more sophisticated approach would allow for multiple agents, each of whom would be pursuing his/her own plan to achieve his/her own goals, and the interaction of those pursuits causing interesting events to occur. TALE-SPIN appears to maintain plans for more than one agent in its simulator. Similarly, the implementation could be extended in this way (other agents might include air traffic control, the passengers, the flight attendants).
Goal revision. At present, the implementation contains shallow rules for revising a goal in the light of new facts. For example, if there is an engine fire, the goal is changed to ‘land as quickly as possible’ but there is no representation of why that is an appropriate new goal. To represent this in a deeper way (and hence tolerate genuinely unexpected happenings), the system would need to explore and compare consequences of doing nothing versus taking various alternative corrective actions. This would need to be a directed search, ie. not simply explore all possible random action sequences in response to an unexpected event, but instead identify what the undesirable outcome was, and identify actions which prevent/reduce the probability of that undesirable outcome. A concept like ‘minimize the time in the air’ would require some thought as to how it could be added to the representation.
First principles planning. All the plans in the implementation are generated ‘from first principles’, using the STRIPS-like planner. While this allows the system to simulate the pilot’s response to many different events, it sometimes seems overkill and counterintuitive: for example, in the simulation the pilot works out from first principles how to get the passengers to Dallas (he/she needs to land there, thus he/she must fly there, thus he/she must take off, …), rather than just simply follow a predefined flight plan. Although scripts were abandoned earlier as the sole means of generating incident scenarios, it seems that there should at least be a place for them in the incident generator (for example, a script might encode the official procedure for response to a particular happening, which the pilot decides to follow). Again, there does not seem to be any major obstacles to including this.
5 Some Related Work Revisited
5.1 Story Grammars
In 1975, Rumelhart published a compelling and influential article, suggesting that story grammars would account for the structure of stories [Rumelhart, 1975]. We offer some additional reflections on his paper here, in particular why a grammar does not appear adequate for story generation.
Similar to our incident representation, Rumelhart essentially suggests that a story is a hierarchical set of events, with goal-based constaints between them. (Slightly modified) examples of his grammar rules include:
|1. Episode Event + Reaction||(An episode might be an event plus a reaction)|
|2. Reaction Plan + Application||(A reaction might be a plan plus its application)|
|3. Application (Preaction)* + Action||(An application might be zero or more preactions, then an action)|
Examples of constraints include that the event initiates the reaction, the plan motivates the application, and the preactions allow the action.
Although this style of grammar partially accounts for our incidents, it has limits as a generative theory. The grammar is rather unconstrained for generation purposes: for example, although stating that “a reaction is a plan plus its application” sounds reasonable, it does not tell the system exactly what constitutes a plan, or how to generate one. The situation- and goal-specific nature of plans suggests a grammar is unlikely to be a good way to generate this next level of detail in the story; rather, a planning algorithm would be needed as a component here, in which case the bulk of the story generation is being off-loaded from a grammar to a planner, with the result that a generative system would end up substantially similar to TALE-SPIN. In addition, Rumelhart’s grammar does not distinguish “good” and “bad” plans (the ‘buggy’ incidents in Section 3 would be considered well-formed stories by Rumelhart’s grammar). In short, the grammar appears to (partly) account for just the top level structure of stories, but does not offer information about constructing the all-important details.
As a top-level account of story structure, Rumelhart’s grammar models events as having clear causal connections (“A happens, causing B, thus C does D, hence E,…”). This accounts well for some stories (eg. Aesop’s “The Man and the Serpent” fable, which he analyzes). However, it does not account for the introduction of random events (our ‘happenings’), not causally related to earlier events. Nor (as he describes) does it account well for multi-protagonist stories where the execution of multiple plans are interleaved, and possibly interact. Again, in this case, the strict ordering which a grammar imposes seems inappropriate for describing the miriad of ways that parallel plan executions can interleave.
Smith and Witten describe a delightful plan-based story generator, called TAILOR [Smith and Witten, 1991], which has some interesting similarities with our incident generator described here. Like our work (and TAIL-SPIN), stories are generated by constructing and executing rational plans for characters in a (toy) world simulation. Again, like our work, a situation is represented by a set of facts, and actions are represented by their preconditions and effects on that set of facts.
However, there also some interesting differences in the details of TAILOR: First, it’s planning algorithm is more like a game-playing algorithm, forward-chaining from the current situation by trying all possible actions, evaluating the resulting situations using a scoring function, and then trying all possible actions from there etc. In this approach, the evalution function measures how ‘near’ a situation is to helping a character satisfy his/her goal. The planning algorithm is thus aware that some actions produce better results than others, but has no explicit representation of why. This contrasts with our STRIPS-like planner (backward-chaining means-ends analysis), where an action in the plan is explicitly chosen because it enables a subsequent action to be performed. Second, instead of introducing random ‘happenings’ to liven up a story, TAILOR includes a second character (besides the ‘hero’ in the story) who’s job is to thwart the hero as much as possible (although there is no representation of why this antagonist may want to do this). Each character (approximately) takes turns during story generation, and thus the analogy to game-playing is very close – a story is in effect a narrative describing the “game” the characters are playing, the hero trying to achieve a goal, while the antagonist trys to stop him/her at every move. This produces some interesting and sometimes amusing results.
This working note has provided a brief excursion into the issue of ‘story’ (episodic narrative) generation, and discussed three different approaches (scripts, story grammars, and a world-simulation/planning approach) in the context of generating ‘toy’ aviation incident reports. Of these, the last appears to be most effective for story generation, although scripts have been shown useful for story understanding (eg. the systems FRUMP [DeJong, 1979, DeJong, 1977], SAM [Cullingford, 1977], and BORIS [Dyer, 1981]). Common to all three approaches is the need to represent the goal-related and causal links between events, highlighting some key knowledge representation requirements for systems which are to understand episodic narratives in depth.
- [Cullingford, 1977] Cullingford, R. E. (1977). Controlling inference in story understanding. In IJCAI-77, page 17.
- [Dehn, 1981] Dehn, N. (1981). Story generation after TALE-SPIN. In IJCAI-81, volume 1, pages 16–18.
- [DeJong, 1977] DeJong, G. (1977). Skimming newspaper stories by computer. In IJCAI-77, page 16.
- [DeJong, 1979] DeJong, G. (1979). Prediction and substantiation: two processes that comprise understanding. In IJCAI-79, pages 217–222.
- [Dyer, 1981] Dyer, M. G. (1981). $RESTAURANT revisited, or ‘lunch with boris’. In IJCAI-81, pages 234–236.
- [Fikes et al., 1981] Fikes, R., Hart, P., and Nilsson, N. (1981). Learning and executing generalized robot plans. In Webber, B. and Nilsson, N., editors, Readings in AI, pages 231–249. Tioga, Palo Alto, CA.
- [Meehan, 1977] Meehan, J. R. (1977). TALE-SPIN, an interactive program that writes stories. In IJCAI-77, pages 91–98.
- [Rumelhart, 1975] Rumelhart, D. E. (1975). Notes on a schema for stories. In Bobrow, D. G. and Collins, A., editors, Representation and Understanding, pages 211–236. Academic Press, Orlando.
- [Smith and Witten, 1991] Smith, T. C. and Witten, I. H. (1991). A planning mechanism for generating story text. Literary and Linguistic Computing, 6(2):119–126.
- [Wilensky, 1983] Wilensky, R. (1983). Story grammars versus story points. Behavioral and Brain Sciences, 6:579–623. (Paper plus peer commentaries).
Appendix A Prolog Implementation of the Incident Generator
% File: talespin.pl % Author: Peter Clark % Date: Jan 1999 % Purpose: Simple and highly improvised reconstruction of Meehan’s TALE-SPIN % story generator, here applied to aviation incident "stories". This % reconstruction undoubtedly misses out a lot of Meehan’s program, and % also adds in new parts/approaches that Meehan didn’t originally use. talespin :- InitialSituation = [ plocation(passengers1, gate(seattle)), alocation(airplane1, gate(seattle)), flight_path(seattle, chicago), flight_path(chicago, dallas), airplane(airplane1), passengers(passengers1) ], InitialGoal = plocation(passengers1, gate(dallas)), make_best_plan(InitialGoal, InitialSituation, InitialPlan), Prob = 0.3, % Probability of incident occurring at a particular step execute_plan(InitialPlan, InitialSituation, InitialGoal, Prob, StoryActions, _StorySituations), write(’Once upon a time...’), nl, anglify(StoryActions, StoryText), lwrite(StoryText). % ====================================================================== % THE STRIPS-LIKE PLANNER % ====================================================================== % make_plan/3: Simple backward-chaining planner, without a protected goal list. make_best_plan(Goal, Situation, BestPlan) :- findall(Quality-Plan, ( make_plan(Goal,Situation,Plan), plan_quality(Plan,Quality) ), RankedPlans), sort(RankedPlans, OrderedPlans), last(_-BestPlan, OrderedPlans). plan_quality(Plan, Quality) :- length(Plan, Length), % lose 10 points per step ( member(evacuate(_),Plan) -> Cost = 1 ; Cost = 0 ), % lose 1 for evacuating Quality is 100 - Length*10 - Cost. % ---------- make_plan(Goal, Situation, Plan) :- make_plan(Goal, Situation, , _FinalSituation, Plan). make_plan(Goal, Situation, _, Situation, ) :- satisfied(Goal, Situation). % no cut, as maybe multiple solns make_plan(Goal, Situation, GoalStack, NewSituation, Actions) :- \+ satisfied(Goal, Situation), \+ member(Goal, GoalStack), % avoid looping event_definition(action, Action, PCs, Dels, Adds), achieves(PCs, Dels, Adds, Goal), make_plans(PCs, Situation, [Goal|GoalStack], MidSituation, PreActions), apply_effects(Dels, Adds, MidSituation, NewSituation), append(PreActions, [Action], Actions). make_plans(, Situation, _, Situation, ). make_plans([Goal|Goals], Situation, GoalStack, NewSituation, Actions) :- make_plan(Goal, Situation, GoalStack, MidSituation, FirstActions), make_plans(Goals, MidSituation, GoalStack, NewSituation, RestActions), append(FirstActions, RestActions, Actions). achieves(_, _, Adds, Goal) :- % Goal = effect member(Goal, Adds). achieves(_, _, Adds, Goal) :- % Goal = ramification of effect rule(( Goal :- Facts )), subset(Facts, Adds). % ====================================================================== % THE SIMULATOR % This is similar to the planner, *except* it will also throw a spanner % in the works (ie. a happening), requiring replanning. % ====================================================================== execute_plan(, FinalSituation, _, _, , [FinalSituation]) :- !. execute_plan(_Actions, Sitn, Goal, P, [Happening|NextActions], [Sitn|NextSitns]) :- maybe(P), % incident happens!! Abandon old Actions and Goal... !, findall(Happening, ( event_definition(happening,Happening,PCs,_,_), satisfieds(PCs,Sitn) ), % is physically feasible Happenings), rnd_member(Happening, Happenings), % choose a random happening do_event(Happening, Sitn, NewSitn), revise_goal(NewSitn, Goal, NewGoal), make_best_plan(NewGoal, NewSitn, NewActions), execute_plan(NewActions, NewSitn, NewGoal, 0, NextActions, NextSitns). execute_plan([Action|Actions], Sitn, Goal, P, [Action|NextActions], [Sitn|NextSitns]) :- do_event(Action, Sitn, NewSitn), execute_plan(Actions, NewSitn, Goal, P, NextActions, NextSitns). do_event(Event, Situation, NewSituation) :- event_definition(_, Event, PCs, Dels, Adds), apply_effects(Dels, Adds, Situation, NewSituation). % Do the deletes and adds as appropriate apply_effects(Dels, Adds, Situation, NewSituation) :- removes(Dels, Situation, MidSituation), append(Adds, MidSituation, NewSituation). % ====================================================================== % OTHER UTILITIES % ====================================================================== satisfieds(, _). satisfieds([F|Fs], S) :- satisfied(F, S), satisfieds(Fs, S). satisfied(Fact, Situation) :- member(Fact, Situation). satisfied(Fact, Situation) :- rule((Fact :- Facts)), % Fact is a ramification of the world satisfieds(Facts, Situation). % ---------- writing... lwrite(). lwrite([X|Xs]) :- write(’ ’), lwrite2(X), nl, lwrite(Xs). lwrite2(). lwrite2([BitX|BitXs]) :- !, write(BitX), lwrite2(BitXs). lwrite2(X) :- write(X). anglify(, ). anglify([Event|Events], [English|Englishs]) :- event_english(Event, English), anglify(Events, Englishs). % ====================================================================== % GENERAL UTILITIES % ====================================================================== removes(, L, L). removes([R|Rs], L, NewL) :- remove(R, L, MidL), removes(Rs, MidL, NewL). remove(A, [A|B], B). remove(A, [C|B], [C|NewB]) :- remove(A, B, NewB). member(X, [X|_]). member(X, [_|Y]) :- member(X, Y). memberchk(X, Y) :- member(X, Y), !. subset(, _). subset([X|Xs], Ys) :- remove(X, Ys, RestYs), subset(Xs, RestYs). nmember(Elem, List, N) :- nmember(Elem, List, 1, N). nmember(Elem, [Elem|_], N, N). nmember(Elem, [_|List], NSoFar, N) :- NewN is NSoFar + 1, nmember(Elem, List, NewN, N). last(X, [X]). last(A, [_,C|D]) :- last(A, [C|D]). % ---------- Randomization utilities :- dynamic lastrnd/1. lastrnd(0). maybe(P) :- random(R), R < P, !. % succeed with probability P random(R) :- lastrnd(N), rnd_number(N,R), retract(lastrnd(N)), NewN is N + 1, ( NewN >= 20 -> assert(lastrnd(0)) ; assert(lastrnd(NewN)) ). rnd_member(X, Xs) :- length(Xs, L), random(R), N is integer(R*L) + 1, nmember(X, Xs, N), !. rnd_number( 0,0.174232). rnd_number( 1,0.186011). rnd_number( 2,0.951800). rnd_number( 3,0.363587). rnd_number( 4,0.108449). rnd_number( 5,0.848878). rnd_number( 6,0.309133). rnd_number( 7,0.230964). rnd_number( 8,0.639224). rnd_number( 9,0.686739). rnd_number(10,0.781066). rnd_number(11,0.983691). rnd_number(12,0.704568). rnd_number(13,0.636376). rnd_number(14,0.881027). rnd_number(15,0.194111). rnd_number(16,0.449212). rnd_number(17,0.110336). rnd_number(18,0.572139). rnd_number(19,0.149503).
Appendix B Prolog KB for Aviation Incidents
% File: talespin-kb.pl % Author: Peter Clark % Date: Jan 1999 % Purpose: Knowledge Base about Aviation incidents for talespin.pl % ====================================================================== % THE FLIGHT INCIDENT KNOWLEDGE BASE % ====================================================================== event_definition(Type, Event, PCs, Adds, Dels) :- ed(Type, Event, PCs, Adds, Dels, _). event_english(Event, English) :- ed(_, Event, _, _, _, English). % ---------- Routine actions... ---------- ed(action, load(Passengers,Airplane), /*pcs*/ [plocation(Passengers,gate(Airport)),alocation(Airplane,gate(Airport))], /*del*/ [plocation(Passengers,gate(Airport))], /*add*/ [contains(Airplane,Passengers)], /*txt*/ ’The passengers boarded the plane.’ ). ed(action, taxi_to_runway(Airplane), /*pcs*/ [alocation(Airplane,gate(Airport))], /*del*/ [alocation(Airplane,gate(Airport))], /*add*/ [alocation(Airplane,runway(Airport))], /*txt*/ ’The plane taxiied to the runway.’ ). ed(action, take_off(Airplane,Airport), /*pcs*/ [alocation(Airplane,runway(Airport))], /*del*/ [alocation(Airplane,runway(Airport))], /*add*/ [alocation(Airplane,near(Airport))], /*txt*/ [’The plane took off from ’,Airport,’.’]). ed(action, cruise(Airplane,Airport1,Airport2), /*pcs*/ [flight_path(Airport1,Airport2),alocation(Airplane,near(Airport1))], /*del*/ [alocation(Airplane,near(Airport1))], /*add*/ [alocation(Airplane,near(Airport2))], /*txt*/ [’The plane cruised towards ’,Airport2,’.’]). ed(action, land(Airplane,Airport2), /*pcs*/ [alocation(Airplane,near(Airport2))], /*del*/ [alocation(Airplane,near(Airport2))], /*add*/ [alocation(Airplane,runway(Airport2))], /*txt*/ [’The plane landed at ’,Airport2,’.’]). ed(action, taxi_to_gate(Airplane), /*pcs*/ [alocation(Airplane,runway(Airport))], /*del*/ [alocation(Airplane,runway(Airport))], /*add*/ [alocation(Airplane,gate(Airport))], /*txt*/ ’The plane taxiied to the gate.’ ). ed(action, unload(Passengers,Airplane), /*pcs*/ [contains(Airplane,Passengers),alocation(Airplane,gate(Airport))], /*del*/ [contains(Airplane,Passengers)], /*add*/ [plocation(Passengers,gate(Airport))], /*txt*/ ’The passengers disembarked.’ ). % ---------- Emergency actions... ---------- ed(action, evacuate(Airplane), /*pcs*/ [a_on_ground(Airplane),alocation(Airplane,Loc),contains(Airplane,Passengers)], /*del*/ [contains(Airplane,Passengers)], /*add*/ [plocation(Passengers,Loc)], /*txt*/ ’The passengers were evacuated from the plane.’ ). ed(action, emergency_landing(Airplane), /*pcs*/ [alocation(Airplane,near(Airport2))], /*del*/ [alocation(Airplane,near(Airport2))], /*add*/ [alocation(Airplane,on_ground_near(Airport2))], /*txt*/ [’The pilot made an emergency landing near ’,Airport2,’.’]). ed(action, medical_help(Passengers), /*pcs*/ [plocation(Passengers, gate(_))], % any gate /*del*/ , /*add*/ [medical_help(Passengers)], /*txt*/ ’Medical help was provided.’ ). % ---------- Possible happenings... ---------- ed(happening, fire(engine), /*pcs*/ , % can happen anywhere /*del*/ , /*add*/ [on_fire(engine)], /*txt*/ ’The engine caught fire.’ ). % ---------- Possible happenings... ---------- ed(happening, ill_passenger, /*pcs*/ [contains(Airplane,Passengers),passengers(Passengers),airplane(Airplane)], /*del*/ , /*add*/ [ill_passenger], /*txt*/ ’A passenger became very ill.’ ). % Ramifications of facts about the world... rule(( a_on_ground(Airplane) :- [alocation(Airplane,gate(_))] )). rule(( a_on_ground(Airplane) :- [alocation(Airplane,runway(_))] )). rule(( a_on_ground(Airplane) :- [alocation(Airplane,on_ground_near(_))] )). rule(( p_on_ground(Passengers) :- [plocation(Passengers,gate(_))] )). rule(( p_on_ground(Passengers) :- [plocation(Passengers,runway(_))] )). rule(( p_on_ground(Passengers) :- [plocation(Passengers,on_ground_near(_))] )). % Rules for revising the goal revise_goal(Situation, plocation(Passengers,_), Goal) :- % If the engine’s on fire, memberchk(on_fire(engine), Situation), !, % get to the ground asap! Goal = p_on_ground(Passengers). revise_goal(Situation, plocation(Passengers,_), Goal) :- % If a passenger’s ill, memberchk(ill_passenger, Situation), !, % get to a gate somewhere. Goal = medical_help(Passengers). revise_goal(_Situation, Goal, Goal).