Given a sequence of events (or ob]ects), each 'characterized by a set of attributes, the problem considered is to discover a rule characterizing the sequence and able to predict a plausible sequence continuation. The rule, called a sequence-generating rule, is nondeterministic in the sense that it does not necessarily tell exactly which etent must appear next in the sequence, but rather, defines a set of plausible next eents. The basic assumption of the methodology presented here is that the next etent depends solely on the attributes of the previous eents in the sequence. These attributes are either initially given or can be den'td from the initial ones through a chain of inferences. Three basic rule models are employed to guide the search for a sequence.generating rule: decomposition, periodic, and disjunctive normal form (DNF). The search process involves simultaneously transforming the initial sequences to derived sequences and instantiating models to find the best match between the instantiated model and the derived sequence. A program, called SPARC/E, is described that implements most of the methodology a.v applied to discosring sequence generating rules in the card game Eleusis. This game, which models the process of scientiftc discovery, is used as a sottrce of examples for illustrating the performance of SPARC/E.

Abstract. We define the main factor of intelligence as the ability to comprehend, formalising this ability with the help of new constructs based on descriptional complexity. The result is a comprehension test, or C-test, exclusively defined in terms of universal descriptional machines (e.g universal Turing machines). Despite the absolute and non-anthropomorphic character of the test it is equally applicable to both humans and machines. Moreover, it correlates with classical psychometric tests, thus establishing the first firm connection between information theoretic notions and traditional IQ tests. The Turing Test is compared with the C-test and their joint combination is discussed. As a result, the idea of the Turing Test as a practical test of intelligence should be left behind, and substituted by computational and factorial tests of different cognitive abilities, a much more useful approach for artificial intelligence progress and for many other intriguing questions that are presented beyond the Turing Test.

Machine Due to the current technology of the computers we can use, we have chosen an extremely abridged emulation of the machine that will effectively run the programs, instead of more proper languages, like l-calculus (or LISP). We have adapted the "toy RISC" machine of [Hernndez & Hernndez 1993] with two remarkable features inherited from its object-oriented coding in C++: it is easily tunable for our needs, and it is efficient. We have made it even more reduced, removing any operand in the instruction set, even for the loop operations. We have only three registers which are AX (the accumulator), BX and CX. The operations Q b we have used for our experiment are in Table 1: LOOPTOP Decrements CX. If it is not equal to the first element jump to the program top.

The likelihood principle states that the visual system prefers the most likely interpretation of a stimulus, whereas the simplicity principle states that it prefers the most simple interpretation. This study investi-gates how close these seemingly very different principles are by combining findings from classical, algorithmic, and structural information theory. It is argued that, in visual perception, the two principles are perhaps very different with respect to the viewpoint-independent aspects of perception but probably very close with respect to the viewpoint-dependent aspects which, moreover, seem decisive in everyday perception. This implies that either principle may have guided the evolution of visual systems and that the simplicity paradigm may provide perception models with the necessary quantitative specifications of the often plausible but also intuitive ideas provided by the likelihood paradigm. In visual perception research, an ongoing debate concerns the question of whether the likelihood principle (Von Helmholtz, 1909/1962) or the simplicity principle (Hochberg & McAlister, 1953) provides the best explanation of the human interpretation of visual stimuli. The phenomenon to be explained is, more specifi-cally, that human subjects usually show a clear preference for only

We present a statistical account of human randomness judgments that uses the idea of algorithmic complexity. We show that an existing measure of the randomness of a sequence corresponds to the assumption that non-random sequences are generated by a particular probabilistic finite state automaton, and use this as the basis for an account that evaluates randomness in terms of the length of programs for machines at di#erent levels of the Chomsky hierarchy.

Contemporary theories of learning postulate one or at most a small number of different learning mechanisms. However, people are capable of mastering a given task through qualitatively different learning paths such as learning by instruction and learning by doing. We hypothesize that the knowledge acquired through such alternative paths differs with respect to the level of abstraction and the balance between declarative and procedural knowledge. In a laboratory experiment we investigated what was learned about patterned letter sequences via either direct instruction in the relevant patterns or practice in solving letter-sequence extrapolation problems. Results showed that both types of learning led to mastery of the target task as measured by accuracy performance. However, behavioral differences emerged in how participants applied their knowledge. Participants given instruction showed more variability in the types of strategies they used to articulate their knowledge as well as longer solution times for generating the action implications of that knowledge as compared to the participants given practice. Results are discussed regarding the implications for transfer, generalization, and procedural application. Learning theories that claim generality should be tested against cross-scenario phenomena, not just parametric variations of a single learning scenario.

"It is part of our thesis that concepts in the strict sense of the term, as we know them- which, since Euler, the great mathematician (1707-1.783), ore represented by circles, a fact which means far more than meets the eye- are foreign to the Chinese mind. "- Gustav Herdan, Linguistics No. 28 Summary In this paper w. present a general data structure for a semantic memory, and we give a definition of "analogy " between items of semantic

We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events while forming mental representations. These judgments may result from the production of reduced memory representations that retain only the musical gist. In a study of improvised music performance, pianists produced variations on melodies. Analyses of the musical events retained across variations provided support for the reductionist account of structural importance. A neural network trained to produce reduced memory representations for the same melodies represented structurally important events more efficiently than others. Agreement among the musicians' improvisations, the network model, and music-theoretic predictions suggest that perceived constancy across musical variation is a natural result of a reductionist mechanism for p...

Since early in the 20th century, simplicity has been considered a relevant factor in visual form and object perception, albeit with some ups and downs. Since the 1990s, other sciences have also shown an interest in simplicity as a driving modelling factor. This recent interest has been triggered by intriguing findings in a mathematical research line, called algorithmic information theory (AIT), that started in the mid 1960s. As I argue here, these AIT findings support but cannot replace the independent perceptual research line that started in the early 1950s with Hochberg and McAlister’s (1953) information-theoretic simplicity idea. In the aftermath of Shannon’s (1948) ground-breaking work in classical information theory, Hochberg and McAlister (1953) proposed modelling the visual system as selecting the most simple interpretation of a proximal stimu-lus. More precisely, they proposed: the less the amount of information needed to define a given organization as compared to the other alternatives, the more likely that the figure will be so perceived. (p. 361)

A more general kind of sequence-prediction problent--the nondetermiuistic prediction problem--is defined, and a general methodology for its sohltion presented. The methodology, called SPARe, employs multiple description models to guide the seamh for plausible sequence-generating rules. Three different models are presented along with algorithms for instantiating them to discover rules. qhe instantiation process requires that the initial input sequence be substantially transformed to make explicit important features of the sequence. Four different data transfmmation operators are described. Tile architecture of a system called SPARC/E [s presented, which implements most of the methodology for discovering scqnence- generating rules in the card game Eleusis. Examples of the execution of SPARC/E are presented.