A deductive system for proving workflow models from operational procedures

Many modern business environments employ software to automate the delivery of workflows; whereas, workflow design and generation remains a laborious technical task for domain specialists. Several differ- ent approaches have been proposed for deriving workflow models. Some approaches rely on process data mining approaches, whereas others have proposed derivations of workflow models from operational struc- tures, domain specific knowledge or workflow model compositions from knowledge-bases. Many approaches draw on principles from automatic planning, but conceptual in context and lack mathematical justification. In this paper we present a mathematical framework for deducing tasks in workflow models from plans in mechanistic or strongly controlled work environments, with a focus around automatic plan generations. In addition, we prove an associative composition operator that permits crisp hierarchical task compositions for workflow models through a set of mathematical deduction rules. The result is a logical framework that can be used to prove tasks in workflow hierarchies from operational information about work processes and machine configurations in controlled or mechanistic work environments.

Deductive, inductive and analogical reasoning in legal decision support systems

In this paper we provide an overview of a number of fundamental reasoning formalisms in artificial intelligence which can and have been used in modelling legal reasoning. We describe deduction, induction and analogical reasoning formalisms, and show how they can be used separately to model legal reasoning. We argue that these formalisms can be used together to model legal reasoning more accurately, and describe a number of attempts to integrate the approaches.

The suppression of q card selections: Evidence for deductive inference in Wason's Selection Task

The results of three experiments investigating the role of deductive inference in Wason's selection task are reported. In Experiment 1, participants received either a standard one-rule problem or a task containing a second rule, which specified an alternative antecedent. Both groups of participants were asked to select those cards that they considered were necessary to test whether the rule common to both problems was true or false. The results showed a significant suppression of q card selections in the two-rule condition. In addition there was weak evidence for both decreased p selection and increased not-q selection. In Experiment 2 we again manipulated number of rules and found suppression of q card selections only. Finally, in Experiment 3 we compared one- and two-rule conditions with a two-rule condition where the second rule specified two alternative antecedents in the form of a disjunction. The q card selections were suppressed in both of the two-rule conditions but there was no effect of whether the second rule contained one or two alternative antecedents. We argue that our results support the claim that people make inferences about the unseen side of the cards when engaging with the indicative selection task.

Logique deductive et inductive

UANL

System for Automated Deduction (SAD): Linguistic and Deductive Peculiarities

In this paper a state of the art of a system of automated deduction called SAD is described . An architecture of SAD corresponds well to a modern vision of the Evidence Algorithm programme, initiated by Academician V.Glushkov.

Consciousness-raising tasks versus deductive approach : two form-focused instruction types in teaching grammar to Iranian high school EFL learners

Consciousness-raising (CR) task is a new way of teaching grammar developed in communicative contexts although little has been written on the effectiveness of CR tasks in EFL setting. The present study is an attempt to investigate the impact of CR tasks in Iranian EFL setting by comparing them with deductive, grammar lessons common in the Iranian schools. The subjects of this study were 80 EFL pre-university male students who were randomly assigned to an experimental group and a control one. The control group received three ordinary teacher-fronted, deductive lessons, a common way of teaching methodology in Iran, on three grammatical structures (adverb placement, indirect object placement and the use of relative clause). The experimental group, however, was treated with three ‘consciousness-raising’ (CR) tasks dealing with the same target structures. The results showed that in the short-run, CR tasks were as effective as deductive approach in promoting the learners’ grammatical knowledge while in the long-run, the CR group maintained their gains more effectively than the deductive group. The conclusion is that CR tasks can function more effectively than deductive approach if the following conditions are met: (a) performing the consciousness-raising tasks in learners’ L1; (b) providing the learners with feedback whenever they encounter a problem in solving the tasks; (c) grouping the learners in such a way that at least one learner in each group would be more proficient than the other members to help the less proficient ones understand and discover the rules more effectively.

Hydrologic landscape regionalisation using deductive classification and random forests

Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of catchments and continents.

A method of anatomy, descriptive and deductive.

Mode of access: Internet.