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Fire Management Notes Is Published by the Forest Service 01The United Stales Department of Agriculture, Washington, D.C

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Fire Management Notes Is Published by the Forest Service 01The United Stales Department of Agriculture, Washington, D.C United States Department of Agriculture Forest Service Fire Management Volume 49, No.2 1988 Notes United States Department of Fire Agriculture Management Forest Service An international quarterly periodical Volume 49, No.2 Notes devoted to forest fire management 1988 Contents Fire Management Notes is published by the Forest Service 01the United Stales Department of Agriculture, Washington, D.C. 3 Artificial Intelligence Applications to Fire The Secretary of Agriculture has determined that the publication of this periodical is DOll J. Latham necessaryin the transaction 01 the public bI.Isiness requlroo by lawaI this De?anman\. Subscriptions may be obtained from the 6 The Florida Division of Forestry Helicopter Program Superintendent of Documents, U.S. Government Printing Office, Washington, John Mason Glen D,C. 20402 NOTE-· The use of trade, firm, or corporation names in this publication is lor the 8 Prescribed Burning for Cultural Resources iniorma\ion and ccrwewerce o~ Ihe reacer. John E. HUJ1ler Such use does not constitute an official endorsement of any product or service by the U.S. Department 01Agriculture. 10 "SO. Ya Wanna Make a Movie')" Disclaimer: Individual authors are responsible lor the technical accuracy 01the Frank Carroll metenar presented in Fire Management Notes. 13 Building a Command Post That Is Mobile Send suggestions and articles to Chief, Forest Service (Attn: Fire Management Bill Tern' Notes). P.O. 80x 96090, U.S. Department ol Agriculture, Washington, DC 20090-6090. Richard E. Lynq, Secretary 16 An Evaluation of Foam as a Fire Suppressant U.S. Department of Agriculture Paul Scblobolun alld ROil Roc/mel F. Dale Robertson, Chia1 Forest Service 21 South Dakota Strike Teams Help Fight California Fires L.A Amicarelia, Director Fire and Aviation Management Ken Terrill and Greg Krumbach Francis A. Russ, General Manager 23 A Quick Method To Determine Northeastern Woody Fuel Weights Cary Rouse and Donna Paananen 25 Celebrating Research Accomplishments at the Forest Fire Laboratory Roberta M. Burzynski 26 An Overview of the 1987 Wallace Lake Fire. Manitoba Kelvin Hirsch 28 Mapping Fires With the FIRE MOUSE TRAP Duane Dtpert and John R. Warren 31 Correcting an Error in the HP-7IB Fire Behavior CROM Robert E. Burgan and Ronald A. SUSOf{ 33 New McCall Smokejumper Base Dedication Planned Gene Benedict Cover. Gene Benedict, Branch Chief. Fire Management and Recreation. stands before McCaJl Smokcjumpcr Base. on Payette National Forest. ID. See story on p. 33. (Photo courtesy of Eric Bechtcl,Cel1lra) Idaho Star News.] l 2 Fire Management Notes Artificial Intelligence Applications to Fire Don J. Latham Meteorologist/physicist. lntcnnountain Fire Science.'!' Laboratory. USDA Forest Service, Missoufa, MT Just what is artificial intelligence deal of intelligence. including the (AI). and what could it possibly do capability of refusing to carry out a for (or to) fire science and manage­ self-destructive action (see Asirnov's ment? Before getting into uses that , laws of robotics). as we shall sec. will be manifold and Where are we now in the realm of pervasive, Jet's dig into some of the AI" Currently. the field is broken characteristics and definitions of into several overlapping categories. artificial intelligence. The most common divisions are: Many potential or actual users of environmental sensing (vision. hear­ developed to recognize tanks. Does AI will insist that the term is an ing). including pattern matching and that sound like something desirable oxymoron. That is, anything arti­ recognition of things: computer learn­ for fire application? ficial cannot be intelligent, and vice ing and analysis; "natural" language At present. the most useful Al versa. There is a simple operational and linguistics; and reasoning. or technology is the' 'expert system" or definition that gets us around this dif­ knowledge-based systems (. 'expert knowledge-based system. These com­ ficulty: the Turing rest, named after systems"). Does this sound familiar? puter programs usc knowledge bases its inventor. Allan Turing. If you are It should. because it is just what peo­ (composed of facts and rules). inter­ communicating with someone or ple do: input data from the senses (or preters of the facts and rules (called something. and you can't tell from internal sources such as a inference engines), input and output whether thc someone or something is dream). filter and think about the routines. and explanation routines. human or machine. then it is intet­ data. apply known processes and Most of these programs follow long ligcnt. We can expand this test just a remembered data to the result. and and sometimes tortuous reasoning little to include more behavior than act on the result. Some of our actions chains through to a conclusion. They communication. and we have a pretty are "hard-wired;" like reflexes, are good at this. whereas people arc good working definition of artificial some "background" or meta­ not; it is not a survival characteristic. intelligence. along the lines of: if it programs, like driving a car, and Engaging in long reasoning chains walks like a duck. quacks like a some call for much active thought. when threatened by a saber-tooth duck, and looks like a duck. then it like reading this paper. tiger probably would get you eaten. is probably a duck, Each of these divisions of AI is An expert at something has created I don't think there is any question in its embryonic stage. Every step a domain of knowledge about the that we can replicate human thinking forward seems to generate more something. Within that domain. the processes in machinery and usc questions than answers. Robotics. for expert has formed heuristics, or machinery to expand those processes. example. a sort of melding of these rules-of-thumb. to guide activities We've already done this. We will arbitrary divisions of Al• is moving within the domain. If the expert can. build machinery that thinks to match fast. Both military and commercial with the aid of a knowledge 'engi­ the job to be done. Imagine coming interests want robots to work for neer, or without. codify his or her to work one morning and turning on them: robots don't talk back. they knowledge, an expert system can be your desktop thinking machine. only obey orders, are dispensable, and constructed to mimic the behavior of to have it announce that it was call­ cannot sue you. At present. however. the expert in the domain. The cod­ ing in sick. So. we'd probably want robots are limited in capability. A ification and the knowledge base that our desktop machines to be rather robot van has successfully negotiated result are knowledge representation, subservient. On the other hand. sup­ a road, on its own, at about 3 miles one of the major problems in expert pose we send a machine to explore per hour. Remotely piloted helicop­ systems. This process is, of course, the surface of Jupiter. We would ters. using infrared scanners and putting the expert's knowledge into want this machine to have a great knowledge-based systems, arc being machine-understandable format. 1988 Volume 49, Number 2 3 Why not just teach the machine to accept the knowledge and codify it. thus replacing the knowledge engi­ neer with a program'? Attempts to do just that arc ongoing. It is one of the current hot topics in artificial intelligence. Of course. the program. unlike the expert. is not able to extend the knowledge base in other than an ele­ mentary deductive way. Once we have started the program going and it has deduced all the consequences of the knowledge it has in its knowl­ edge base. it stops. We have not told the program how to extend its knowl­ o A scmi-auromntcd land-use plan­ track of wildlife and the like. It edge. The machine's extension of its ning system. could be programmed to do clcmcn­ knowledge is. as you might expect. C!) An elementary, reasoning fire dis­ wry thinking about its environment one aspect of machine learning. The patch system. and would call for help if needed. Of program has to know not only how o Prescribed burning plans that learn. course. it would know where it is to learn. or store data, but also to e Interactive. self-correcting, geo­ and communicate by satellite. form and test new hypotheses about graphic information systems. Fire dispatch machinery such as I new input. To do that. the program Many of these would have been describe is already beginning to be has to know how to think about availablc this year or during the next used, except for the robotics. in thinking. And so on. couple of years on cheap mass stor­ Ontario and Quebec. The package If our understanding of how to age and running on advanced work and concept were developed by Peter huild effective machines is so infan­ stations, large desktop computers. Kourtz at Pctawaw«. The machine tile, then what good. in a practical We are, then at least 5 years behind. dispatcher keeps track of fuels by sense, arc the simple programs that Jf we run Iike heck to get caught satellite, rainbll by radar. and arc available to us'! Right now we lip. how far could we get by the year weather and lightning occurrence by can computerize any repetitive proc­ 2000:) We could be testing or have: land-based sensors. It is at present ess or any closed operation for which e The first crude robots for forest dependent on human input. but will a knowledge representation can be usc. become more and more intelligent as found. We can also generate pro­ o Completely automated fire dispatch time goes on. grams that add facts within the machinery that would be able to Do such machines and programs representation to their knowledge use the forest robots as smoke­ replace people? Yes.
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