Knowledge level learning and the power law: A soar model of skill acquisition in scheduling

Zusammenfassung. Das hier vorgestellte Modell lernt graduell, Planungsaufgaben aus der Klasse der Maschinenbelegungsprobleme (job-shop-scheduling problems) zu lösen. Mit Hilfe des Chunking-Mechanismus von Soar wird episodisches Wissen über die Belegungsreihenfolge von Aufträgen auf Maschinen memoriert. Bei der Entwicklung des Modells wurden zahlreiche qualitative (z. B. Transfereffekte) und quantitative Befunde (z. B. Bearbeitungszeiten) aus einer früheren empirischen Untersuchung berücksichtigt. In einer Validierungsstudie wurden dieselben Aufgaben von 14 Probanden und dem Modell bearbeitet. Die Passung von Simulationsdaten und empirischen Ergebnissen fiel insgesamt gut aus. Allerdings löst das Modell die Aufgaben schneller und zeigt auch einen etwas besseren Lernverlauf als die Probanden. Das Modell liefert eine Erklärung für das Rauschen, das typischerweise bei Bearbeitungszeiten zu beobachten ist: es handelt sich um erworbenes Wissen, das mehr oder weniger gut und auch unterschiedlich häufig auf neue Situationen übertragen wird. Der Lernverlauf der Probanden entspricht nur für aggregierte Daten einer Potenzfunktion (power law). Der vorgestellte Mechanismus zeigt, wie ein symbolisches Modell der Informationsverarbeitung graduelle Verhaltensänderungen generiert und wie der offensichtliche Erwerb allgemeiner Prozeduren ohne explizites Lernen von deklarativen Regeln erfolgen kann. Es wird nahegelegt, daß es sich hier um die Modellierung einer Form impliziten Lernens handelt. Summary. The model presented here gradually learns how to perform a job-shop scheduling task. It uses Soar's chunking mechanism to acquire episodic memories about the order to schedule jobs. The model was based on many qualitative (e.g., transfer effects) and quantitative (e.g., solution time) regularities found in previously collected data. The model was tested with new data where scheduling tasks were given to the model and to 14 subjects. The model generally fit these data with the restrictions that the model performs the task (in simulated time) faster than the subjects, and its performance improves somewhat more quickly than the subjects' performance. The model provides an explanation of the noise typically found in problem solving times - it is the result of learning actual pieces of knowledge that transfer more or less to new situations but rarely by an average amount. Only when the data are averaged (i.e., over subjects) does the smooth power law appear. This mechanism demonstrates how symbolic models can exhibit a gradual change in behavior and how the apparent acquisition of general procedures can be performed without resorting to explicit declarative rule generation. We suggest that this may represent a type of implicit learning.     

The puzzle of language evolution

Linguistics must again concentrate on the evolutionary nature of language, so that language models are more realistic with respect to human natural languages and have a greater explanatory force. Multi-agent systems are proposed as a possible route to develop such evolutionary models and an example is given of a concrete experiment in the origins and evolution of word-meaning based on a multi-agent approach.     

Familiarity and pronounceability of nouns and names

Ratings of familiarity and pronounceability were obtained from a random sample of 199 surnames (selected from over 80,000 entries in the Purdue University phone book) and 199 nouns (from the Ku?era-Francis, 1967, word database). The distributions of ratings for nouns versus names are substantially different: Nouns were rated as more familiar and easier to pronounce than surnames. Frequency and familiarity were more closely related in the proper name pool than the word pool, although both correlations were modest. Ratings of familiarity and pronounceability were highly related for both groups. A production experiment showed that rated pronounceability was highly related to the time taken to produce a name. These data confirm the common belief that there are differences in the statistical and distributional properties of words as compared to proper names. The value of using frequency and the ratings of familiarity and pronounceability for predicting variations in actual pronunciations of words and names are discussed.     

Why Dynamical Self-Excitation Is Possible

In Pérez Laraudogoitia (1996), I introduced a simple example of a supertask that involved the possibility of spontaneous self-excitation and, therefore, of a particularly interesting form of indeterminism in classical dynamics. Alper and Bridger (1998) criticised (among other things) this result. In the present article, I answer their criticisms. In what follows I assume familiarity both with Pérez Laraudogoitia (1996) and Alper and Bridger’s subsequent article. This revised version was published online in June 2006 with corrections to the Cover Date.     

Every Finitely Reducible Logic has the Finite Model Property with Respect to the Class of ♦-Formulae

In this paper a unified framework for dealing with a broad family of propositional multimodal logics is developed. The key tools for presentation of the logics are the notions of closure relation operation and monotonous relation operation. The two classes of logics: FiRe-logics (finitely reducible logics) and LaFiRe-logics (FiRe-logics with local agreement of accessibility relations) are introduced within the proposed framework. Further classes of logics can be handled indirectly by means of suitable translations. It is shown that the logics from these classes have the finite model property with respect to the class of -formulae, i.e. each -formula has a -model iff it has a finite -model. Roughly speaking, a -formula is logically equivalent to a formula in negative normal form without occurrences of modal operators with necessity force. In the proof we introduce a substantial modification of Claudio Cerrato's filtration technique that has been originally designed for graded modal logics. The main core of the proof consists in building adequate restrictions of models while preserving the semantics of the operators used to build terms indexing the modal operators.     

Perceptual restoration of missing sounds in a group of hallucinating schizophrenics.

17 subjects diagnosed with schizophrenia and having auditory hallucinations in their case history were compared with 15 control subjects in an experiment on perceptual restoration. A tone pattern was presented, then interrupted by noise, under conditions such that the tone pattern could be heard as going on continuously (restoration). A series of 16 stimuli with a distractor of varying amplitude (noise) were presented. Healthy controls reliably reported restoration in Presentations 9 and 10. Four schizophrenics reported no restoration at all. Three of them reported restoration earlier than controls, and four others reported it later than controls and continued to report the phenomenon after a point at which no healthy controls did. Six other presented an irregular pattern of response to the phenomenon. The results are discussed with respect to the neurophysiological functioning of the auditory pathway and schizophrenic symptoms.     

Are European and American golf players different? Reply to Engelhardt (1997)

Analysis of 1998 statistics for individual performance in the PGA European Tour yielded significant differences between some shot-making skills (drive distance, total driving, greens in regulation, and sand saves) between the top 10 and bottom 10 money winners, replicating (with partially different results) a result found for the 1995 American PGA Tour by Engelhardt.