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

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

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

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

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 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