An architecture for a rational agent must allow for means-end reasoning, for the weighing of competing alternatives, and for interactions betwen these two forms of reasoning. Such an architecture must also address the problem of resource boundedness. We sketch a solution of the first problem that points the way to a solution of the second. In particular, we present a high-level specification of the practical-reasoning component of an architecture for a resource-bounded rational agent. In this architecture, a major role of the agent's plans is to constrain the amount of further practical reasoning she must perform.L'architecture d'un agent rationnel doit permettre le raisonnement procidant des fins aux moyens, le choix entre diffkrentes actions possibles, et I'interaction entre ces deux modes de raisonnement. Elle doit aussi tenir compte des consiquences des limites de ressources disponibles. Nous esquissons ici une solution au premier problhe qui indique comment on pounait risoudre le second. Nous proposons, en particulier, une spicification abstraite d'un module de gineration de plans pour un agent rationnel dont les ressources sont bornies. Dans cette architecture, le r6le principal des plans d'un agent est de limiter les ressources devant &tre consacris au raisonnement.
FASTUS is a system for extracting information from free text in English, and potentially other languages as well, for entry into a database, and potentially for other applications. It works essentially as a cascaded, nondeterministic finite state automaton. There are four steps in the operation of FASTUS. In Step 1 sentences are scanned for certain trigger words to determine whether further processing should be done. In Step 2 noun groups, verb groups, and prepositions and some other particles are recognized. The input to Step 3 is the sequence of phrases recognized in Step 2; patterns of interest are identified in Step 3 and corresponding "incident structures" are built up. In Step 4 incident structures that derive from the same incident are identified and merged, and these are used in generating database entries. FASTUS is an order of magnitude faster than any comparable system; it can process a news report in an average of less than eleven seconds. This translates directly into fast development time. In the three and a half weeks between its first use and the MUC-4 evaluation in May 1992, we were able to build up its domain knowledge to a point where it was among the leaders in the evaluation.
INTRODUCTIO NSRI International participated in the MUC-6 evaluation using the latest version of SRI's FASTUS system [1] . The FASTUS system was originally developed for participation in the MUC-4 evaluatio n [3] in 1992, and the performance of FASTUS in MUC-4 helped demonstrate the viability of finit e state technologies in constrained natural-language understanding tasks . The system has undergon e significant revision since MUC-4, and it is safe to say that the current system does not share a singl e line of code with the original . The fundamental ideas behind FASTUS, however, are retained i n the current system : an architecture consisting of cascaded finite state transducers, each providin g an additional level of analysis of the input, together with merging of the final results . This paper will describe the version of the FASTUS system employed in MUC-6 and highlight the innovations that distinguish it from previous versions described in the literature . SRI used the FASTUS system for each of the MUC-6 tasks : the named entity task, the templateentity task, the coreference task, and the scenario template task . Because a single system, with a single configuration, was used to run all the tasks, and because the first three tasks are in som e sense prerequisites to the fourth, we will focus our attention in this paper on the scenario templat e task . BASIC FASTUSThe SRI FASTUS system is based on a series of finite-state transducers that compute the transformation of text from sequences of characters to domain templates . This architecture has proven t o be very flexible, and has been applied with success to a number of different information extractio n tasks in widely varying domains . We have applied FASTUS to extraction of information about terrorist incidents [3], extraction of information about joint ventures [2], indexing of legal document s for hypertext, extracting extensive information from military texts (Warbreaker Message Handler) , extraction of information from spoken dialogues [4], and a number of other smaller systems an d pilot applications . We have applied FASTUS to Japanese texts [2, 4] as well as English .Each transducer (or "phase") in the series takes the output of the previous phase and map s it into structures that comprise the input to the next phase, or that contain the domain templat e
The electromyograms (EMG) of shivering human subjects exposed to 0 degrees C air in an environmental chamber were analyzed to detect slow-amplitude modulations (SAMs, less than 1 Hz) in the EMGs of widely separated muscles and to study the relationship of these SAMs to respiration rate and skin temperature. Distinct amplitude modulations were observed in the raw EMGs during shivering. The peaks in EMG activity occurred simultaneously in the majority of the monitored muscles in all subjects. Pearson correlations between the average rectified EMGs of 93% of the muscles were significant (P less than 0.05). Visual analysis of the EMG and respiration signals indicated that the peaks in muscular activity occurred 6-12 times/min, whereas respiration ranged from 10 to 23 cycles/min. For all subjects respiration was at a higher frequency than amplitude modulation in the EMG. Comparison of EMG records with expiratory flow rate traces in shivering subjects indicated no one-to-one correlation between the occurrence of respiration and EMG amplitude modulations. Respiratory flow rate and average rectified EMG showed significant correlation in only 33% of the cases. In addition, skin temperature changes could not be correlated with the SAMS.
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