230 research outputs found
The Evolution of Spacing Effects in Autonomous Agents
This paper discusses research into whether the memories of adaptive autonomous agents can be made to spontaneously evolve spacing effects. Experiments involving human memory have shown that learning trials massed closely in time elicit slower learning than the equivalent number trials spaced apart in time. These "spacing effects" have been observed across a wide array of conditions. The experimental results detailed here show that such effects can be made to evolve spontaneously in autonomous agents. The results also suggest that the greater learning difficulty humans experience from closely spaced trials may not be the result of a defect of biology, but rather may be a consequence of a need to give only the appropriate weight to each learning experience
Free will as private determinism
This article suggests that our sense of free will is formed when others react to our behavior with surprise, even though our private knowledge tells us our behavior was determined by our preferences. Such surprised reactions, even when our behavior is from our perspective fully determined, lead us to infer that we exercise free will
The Evolution of Semantic Memory and Spreading Activation
The purpose of this paper is to demonstrate that it is possible to deduce the structure of human semantic memory by mathematically analyzing the environment which through evolution has shaped it. The theory arrived at is similar to the spreading-activation theories of Quillian, and Collins and Loftus, but it contrasts with the above in that it involves a rigidly restricted activation that employs two distinct types of linking and three distinct types of intersection search. These three types of intersection are then used to explain the facilitation of lexical decisions, the nature of polysemy, riddles, several production experiments by Loftus, as well as the effect of word order on meaning and paired-associate learning
Coincidence, data compression, and Mach’s concept of “economy of thought”
A case is made that Mach’s principle of “economy of thought”, and therefore usefulness, is related to the compressibility of data, but that a mathematical expression may compress data for reasons that are sometimes coincidental and sometimes not. An expression, therefore, may be sometimes explainable and sometimes not. A method is proposed for distinguishing coincidental data compression from non-coincidental, where this method may serve as a guide in uncovering new mathematical relationships. The method works by producing a probability that a given mathematical expression achieves its compression purely by chance
Multiple-Goal Heuristic Search
This paper presents a new framework for anytime heuristic search where the
task is to achieve as many goals as possible within the allocated resources. We
show the inadequacy of traditional distance-estimation heuristics for tasks of
this type and present alternative heuristics that are more appropriate for
multiple-goal search. In particular, we introduce the marginal-utility
heuristic, which estimates the cost and the benefit of exploring a subtree
below a search node. We developed two methods for online learning of the
marginal-utility heuristic. One is based on local similarity of the partial
marginal utility of sibling nodes, and the other generalizes marginal-utility
over the state feature space. We apply our adaptive and non-adaptive
multiple-goal search algorithms to several problems, including focused
crawling, and show their superiority over existing methods
The Battle of the Little Bighorn in Finnegans Wake
This article shows that underlying the Museyroom passage of James Joyce's Finnegans Wake is an account of the Battle of the Little Bighorn. Joyce's passage touches on what motivated the battle, its Irish participants, its commanders, Custer's disagreements with his Crow scouts, Sitting Bull's prediction of Custer's failure, Custer's (purely fanciful) joking reply to Sitting Bull's prediction, Custer's home near Bismarck, ND, and his signature song. Other passages scattered throughout Finnegans Wake treat of casualties, a straggler, Custer's trumpeter, Custer's from-the-grave denunciations of his subordinates, and the burial of the dead. The famous "Three quarks for Muster Mark!" passage describes the desolation of Last Stand Hill. And Joyce's famous final paragraph contains an account of the legend of the narrow escape of Custer's Crow scout Ashishishe, who, at the battle's close, hides in the carcass of a horse before making his escape downriver. A brief look at Joyce's choice of names closes out the article
The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference
It is common to view programs as a combination of logic and control: the
logic part defines what the program must do, the control part -- how to do it.
The Logic Programming paradigm was developed with the intention of separating
the logic from the control. Recently, extensive research has been conducted on
automatic generation of control for logic programs. Only a few of these works
considered the issue of automatic generation of control for improving the
efficiency of logic programs. In this paper we present a novel algorithm for
automatic finding of lowest-cost subgoal orderings. The algorithm works using
the divide-and-conquer strategy. The given set of subgoals is partitioned into
smaller sets, based on co-occurrence of free variables. The subsets are ordered
recursively and merged, yielding a provably optimal order. We experimentally
demonstrate the utility of the algorithm by testing it in several domains, and
discuss the possibilities of its cooperation with other existing methods
The Psychology of The Two Envelope Problem
This article concerns the psychology of the paradoxical Two Envelope Problem. The goal is to find instructive variants of the envelope switching problem that are capable of clear-cut resolution, while still retaining paradoxical features. By relocating the original problem into different contexts involving commutes and playing cards the reader is presented with a succession of resolved paradoxes that reduce the confusion arising from the parent paradox. The goal is to reduce confusion by understanding how we sometimes misread mathematical statements; or, to completely avoid confusion, either by reforming language, or adopting an unambiguous notation for switching problems. This article also suggests that an illusion close in character to the figure/ground illusion hampers our understanding of switching problems in general and helps account for the intense confusion that switching problems sometimes generate
A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle
One of the most common mechanisms used for speeding up problem solvers is
macro-learning. Macros are sequences of basic operators acquired during problem
solving. Macros are used by the problem solver as if they were basic operators.
The major problem that macro-learning presents is the vast number of macros
that are available for acquisition. Macros increase the branching factor of the
search space and can severely degrade problem-solving efficiency. To make macro
learning useful, a program must be selective in acquiring and utilizing macros.
This paper describes a general method for selective acquisition of macros.
Solvable training problems are generated in increasing order of difficulty. The
only macros acquired are those that take the problem solver out of a local
minimum to a better state. The utility of the method is demonstrated in several
domains, including the domain of NxN sliding-tile puzzles. After learning on
small puzzles, the system is able to efficiently solve puzzles of any size.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources
The performance of anytime algorithms can be improved by simultaneously
solving several instances of algorithm-problem pairs. These pairs may include
different instances of a problem (such as starting from a different initial
state), different algorithms (if several alternatives exist), or several runs
of the same algorithm (for non-deterministic algorithms). In this paper we
present a methodology for designing an optimal scheduling policy based on the
statistical characteristics of the algorithms involved. We formally analyze the
case where the processes share resources (a single-processor model), and
provide an algorithm for optimal scheduling. We analyze, theoretically and
empirically, the behavior of our scheduling algorithm for various distribution
types. Finally, we present empirical results of applying our scheduling
algorithm to the Latin Square problem
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