Solvable Tasks Research Articles

Practice makes perfect: familiarity of task determines success in solvable tasks for free-ranging dogs (Canis lupus familiaris).

Domestic dogs' (Canis lupus familiaris) socio-cognitive faculties have made them highly sensitive to human social cues. While dogs often excel at understanding human communicative gestures, they perform comparatively poorly in problem-solving and physical reasoning tasks. This difference in their behaviour could be due to the lifestyle and intense socialization, where problem solving and physical cognition are less important than social cognition. Free-ranging dogs live in human-dominated environments, not under human supervision and are less socialized. Being scavengers, they often encounter challenges where problem solving is required in order to get access to food. We tested Indian street dogs in familiar and unfamiliar independent solvable tasks and quantified their persistence and dependence on a novel human experimenter, in addition to their success in solving a task. Our results indicate that free-ranging dogs succeeded and persisted more in the familiar task as compared to the unfamiliar one. They showed negligible amount of human dependence in the familiar task, but showed prolonged gazing and considerable begging behaviour to the human experimenter in the context of the unfamiliar task. Cognitive abilities of free-ranging dogs thus play a pivotal role in determining task-associated behaviours based on familiarity. In addition to that, these dogs inherently tend to socialize with and depend on humans, even if they are strangers. Our results also illustrate free-ranging dogs' low competence at physical cognitive tasks.

Persistent Memory in Single Node Delay-Coupled Reservoir Computing

Delays are ubiquitous in biological systems, ranging from genetic regulatory networks and synaptic conductances, to predator/pray population interactions. The evidence is mounting, not only to the presence of delays as physical constraints in signal propagation speed, but also to their functional role in providing dynamical diversity to the systems that comprise them. The latter observation in biological systems inspired the recent development of a computational architecture that harnesses this dynamical diversity, by delay-coupling a single nonlinear element to itself. This architecture is a particular realization of Reservoir Computing, where stimuli are injected into the system in time rather than in space as is the case with classical recurrent neural network realizations. This architecture also exhibits an internal memory which fades in time, an important prerequisite to the functioning of any reservoir computing device. However, fading memory is also a limitation to any computation that requires persistent storage. In order to overcome this limitation, the current work introduces an extended version to the single node Delay-Coupled Reservoir, that is based on trained linear feedback. We show by numerical simulations that adding task-specific linear feedback to the single node Delay-Coupled Reservoir extends the class of solvable tasks to those that require nonfading memory. We demonstrate, through several case studies, the ability of the extended system to carry out complex nonlinear computations that depend on past information, whereas the computational power of the system with fading memory alone quickly deteriorates. Our findings provide the theoretical basis for future physical realizations of a biologically-inspired ultrafast computing device with extended functionality.

The Smart Technologies for Movement of the Robotic Truck Caravan

The report deals with the technologies developed to control robotic trucks belonging to an autonomous truck caravan. Solvable tasks, the structure of nodes and the work principle of the autonomous freight vehicle smart control system are listed.

Socially Positive Behaviors as Self-Handicapping

Typical studies of self-handicapping operationalize the construct narrowly by examining obviously debilitating and undesirable behaviors. We ask whether socially positive behaviors, including helping others, might serve the same function but be under-appreciated. Using a standard self-handicapping paradigm, 105 females undertook a difficult tracing task allegedly related to intelligence. They received either success feedback on tasks that were partially unsolvable (Noncontingent Success Unsolvable—NSU), no feedback on those same tasks (NF), or success feedback on solvable tasks (Noncontingent Success Solvable—NSS). As predicted, during a practice period before expecting to re-take a longer version of the task, NSU (vs. NF and NSS) participants were more likely to help a frustrated confederate completing another task and spent more time engaging with the confederate, but less time practicing. Implications are discussed for self-handicapping theory and for psychopathology.

First experiments with PowerPlay

Like a scientist or a playing child, PowerPlay (Schmidhuber, 2011) not only learns new skills to solve given problems, but also invents new interesting problems by itself. By design, it continually comes up with the fastest to find, initially novel, but eventually solvable tasks. It also continually simplifies or compresses or speeds up solutions to previous tasks. Here we describe first experiments with PowerPlay. A self-delimiting recurrent neural network SLIM RNN (Schmidhuber, 2012) is used as a general computational problem solving architecture. Its connection weights can encode arbitrary, self-delimiting, halting or non-halting programs affecting both environment (through effectors) and internal states encoding abstractions of event sequences. Our PowerPlay-driven SLIM RNN learns to become an increasingly general solver of self-invented problems, continually adding new problem solving procedures to its growing skill repertoire. Extending a recent conference paper (Srivastava, Steunebrink, Stollenga, & Schmidhuber, 2012), we identify interesting, emerging, developmental stages of our open-ended system. We also show how it automatically self-modularizes, frequently re-using code for previously invented skills, always trying to invent novel tasks that can be quickly validated because they do not require too many weight changes affecting too many previous tasks.

Structure Elucidator: A Versatile Expert System for Molecular Structure Elucidation from 1D and 2D NMR Data and Molecular Fragments

StrucEluc is an expert system that allows the computer-assisted elucidation of chemical structures based on the inputs of a series of spectral data including 1D and 2D NMR and mass spectra. The system has been enabled to allow a chemist to utilize fragments stored in a fragment database as well as user-defined fragments submitted by the chemist in the structure elucidation process. The association of fragments in this way has been shown to dramatically speed up the process of structure generation from 2D NMR data and has helped to minimize or eliminate the need for user intervention thereby further enabling the vision of automated elucidation. The use of fragments has frequently transformed very difficult 2D NMR elucidation challenges into easily solvable tasks. A strategy to utilize molecular fragments has been developed and optimized based on specific challenging examples. This strategy will be described here using real world examples. Experience gained by solving more than 150 structure elucidation problems from a variety of literature sources is also reviewed in this work.

Structure Elucidator: a versatile expert system for molecular structure elucidation from 1D and 2D NMR data and molecular fragments.

StrucEluc is an expert system that allows the computer-assisted elucidation of chemical structures based on the inputs of a series of spectral data including 1D and 2D NMR and mass spectra. The system has been enabled to allow a chemist to utilize fragments stored in a fragment database as well as user-defined fragments submitted by the chemist in the structure elucidation process. The association of fragments in this way has been shown to dramatically speed up the process of structure generation from 2D NMR data and has helped to minimize or eliminate the need for user intervention thereby further enabling the vision of automated elucidation. The use of fragments has frequently transformed very difficult 2D NMR elucidation challenges into easily solvable tasks. A strategy to utilize molecular fragments has been developed and optimized based on specific challenging examples. This strategy will be described here using real world examples. Experience gained by solving more than 150 structure elucidation problems from a variety of literature sources is also reviewed in this work.

A Newtonian Iteration Scheme With the Schwinger Variational Functional for Solving a Scattering Problem

A Newtonian iteration scheme has been constructed for solving a scattering problem using the Schwinger variational functional. The scattering problem is formulated as an eigenvalue problem with respect to a pair of unknown variables: a phase shift and a wavefunction. The efficiency and accuracy of the proposed iteration scheme are demonstrated on exact solvable tasks of an elastic scattering problem with Morse and spherical well potentials.

The Combinatorial Structure of Wait-Free Solvable Tasks

This paper presents a self-contained study of wait-free solvable tasks. A new necessary condition for wait-free solvability, based on a restricted set of executions, is proved. This set of executions induces a very simple-to-understand structure, which is used to prove tight bounds for k-set consensus and renaming. The framework is based on topology, but uses only elementary combinatorics, and, in contrast to previous works, does not rely on algebraic or geometric arguments.

Uncontrollability as irreducible uncertainty

This paper tests a prediction from the information-processing model of helplessness (Sedek & Kofta, 1990) that during exposure to uncontrollability people experience high levels of irreducible uncertainty. Participants were given either a solvable or unsolvable discrimination task consisting of five problems. After completion of each problem participants evaluated the probability of all solution hypotheses. Three times during the course of each problem, participants indicated the solution hypotheses they were considering at that point. As predicted (1) entropy of the hypothesis set (the uncertainty measure) was higher under unsolvable than solvable tasks; (2) a gradual reduction in the number of hypotheses was noted in the solvable but not unsolvable task condition; and (3) uncertainty was a reliable predictor of self-reported cognitive difficulties with thinking production and attention. Copyright © 1999 John Wiley & Sons, Ltd.

Optimism and Self-Mastery Predict More Rapid Disengagement from Unsolvable Tasks in the Presence of Alternatives

Do favorable beliefs about the self, the future, and one's degree of control over events compromise one's ability to recognize what one cannot accomplish? Previous studies indicating that people with favorable self-beliefs spend more time on unsolvable tasks typically examined persistence with respect to a single unsolvable task or set of tasks. To test whether such seemingly maladaptive persistence would occur in the presence of alternative tasks, we varied whether an initial set of seven unsolvable anagrams was followed by 14 solvable anagrams and examined problem-solving efforts among college students (N = 96) given a 20-min time limit. In the absence of alternatives, most participants worked on the unsolvable trials until the end of the time limit; however, in the presence of alternatives, participants high in optimism or self-mastery beliefs who were not allowed to return to previous trials disengaged from the unsolvable anagrams nearly 4 min sooner than participants low in such beliefs. Additionally, optimists tended to outperform participants low in optimism on the subsequent solvable trials when these trials were said to test an aspect of verbal intelligence different from the initial set. These results suggest that people high in optimism and self-mastery are able to disengage from unsolvable tasks in order to allocate effort to solvable tasks. Implications for the study of nonproductive persistence, the adaptiveness of positive beliefs, and the modification of coping efforts are discussed.

Three-Processor Tasks Are Undecidable

We show that no algorithm exists for deciding whether a finite task for three or more processors is wait-free solvable in the asynchronous read-write shared-memory model. This impossibility result implies that there is no constructive (recursive) characterization of wait-free solvable tasks. It also applies to other shared-memory models of distributed computing, such as the comparison-based model.

Self‐Deceptive Coping: Adaptive Only in Ambiguous Contexts

ABSTRACT Two experiments investigated claims for the efficacy of self‐deceptive coping (e.g., Sackeim, 1983, 1988). In Study I the performance of self‐deceivers on solvable anagrams was found to he remarkably poor relative to that of non‐self‐deceivers after both groups were exposed to unsolvable problems. The starkly unambiguous failure experience may have precluded self‐deception. Therefore, in Study 2 participants were exposed to unsolvable problems either with or without an excuse. Self‐deceivers who encountered failure with an excuse subsequently performed much better on the solvable tasks than those without an excuse. These findings suggest that the use of self‐deception following threat is constrained by the availability of contextual ambiguity (e.g., excuses). The effect of the excuse was not related to participants' mood or attributions for performance.

Beyond Beliefs: Parent and Child Behaviors and Children's Perceived Academic Competence

Examined the relations of parent and child behaviors with children's perceptions of their academic competence. 74 high-achieving third-grade children, with varying levels of perceived academic competence, were observed working with mothers and fathers both on solvable tasks (Period 1) and during a period that included some unsolvable tasks (Period 2). Results indicated that children's perceived academic competence was positively related to father warmth, both at Period 1 and at Period 2. Children's perceived academic competence was also positively related to their own behavior when working with fathers at Period 2. Specifically, children with higher perceived academic competence showed more emotional restraint and were more self-reliant when working on tasks at Period 2 than were children with lower perceived academic competence. The results indicate that there are systematic, observable correlates of children's self-reports of their perceived academic competence.

Beyond Beliefs: Parent and Child Behaviors and Children's Perceived Academic Competence

Examined the relations of parent and child behaviors with children's perceptions of their academic competence. 74 high-achieving third-grade children, with varying levels of perceived academic competence, were observed working with mothers and fathers both on solvable tasks (Period 1) and during a period that included some unsolvable tasks (Period 2). Results indicated that children's perceived academic competence was positively related to father warmth, both at Period 1 and at Period 2. Children's perceived academic competence was also positively related to their own behavior when working with fathers at Period 2. Specifically, children with higher perceived academic competence showed more emotional restraint and were more self-reliant when working on tasks at Period 2 than were children with lower perceived academic competence. The results indicate that there are systematic, observable correlates of children's self-reports of their perceived academic competence.

Randomization in Robot Tasks

This article explores the role of randomization in the solution of robot manipulation tasks. Randomization refers to the ran dom selection and execution of an action from a collection of possible actions. The intention is that this collection contains some actions that are useful for making progress toward ac complishing a task, but that the precise identity of these useful actions is unknown. Randomization offers one approach for ensuring progress in a probabilistic sense. An example of randomization is given by the strategy of shaking a bin containing a part in order to orient the part in a desired stable state with some high probability. Another exam ple consists of using reliable sensory information to bring two parts close together, then relying on short random motions to actually mate the two parts once the part motions lie below the available sensing resolution. Further examples include tapping parts that are tightly wedged, twirling gears before trying to mesh them, and vibrating parts to facilitate a mating operation. Randomization is also useful for mobile robot navigation and as a means of guiding the design process. Randomization is useful in three basic ways. First, random ization can increase the class of solvable tasks. This is because a randomized strategy need not guarantee task success with certainty in a specific number of steps, relying instead on re peated execution of the randomizing actions to accomplish the task in an expected sense. Second. randomization can reduce a strategy's knowledge requirements. This is because randomiza tion can tolerate and circumvent local failures, thereby making a strategy less sensitive to task details. Third, randomization can simplify the planning and execution process. This is be cause a randomized solution may be able to ignore precise prediction of special case scenarios, instead simply ensuring eventual accomplishment of the task independent of the actual scenarios encountered.

On Iteration in Robot Planning

The paper discusses the role of iteration in robot plans constructed in the framework of predicate logic. It is shown that, unless some special axioms are added, the class of solvable tasks is not enlarged by admitting iteration in plans.