Tuesday , February 7 2023

Computational Environment Generation for Computer Algebra Systems

Institute of Mathematics and Computer Science, Academy of Sciences of Moldova
Str. Academiei, 5, Chişinău, MD 2028, Republic of Moldova

Abstract: This paper deals with the problem of user interface development for Computer Algebra Systems. An algorithm of computational environment generation based on inferences from users’s actions is proposed.

Keywords: Intelligent interface, computer algebra systems, adaptation.

Svetlana Cojocaru – Dr. h., is the deputy director of the Institute of Mathematics and Computer Science, Academy of Science of Moldova. S. Cojocaru has published 3 books, 5 chapters in books, more than 100 papers. Her research interests include natural language processing, computer algebra, biological computing.

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Svetlana COJOCARU, Computational Environment Generation for Computer Algebra Systems, Studies in Informatics and Control, ISSN 1220-1766, vol. 17 (2), pp. 231-236, 2008.

1. Introduction

The software complexity is permanently increasing and it requires from the user more vast and deep knowledge about systems functioning. Interfaces, which realize the human-computer interaction, take upon themselves to itself also a part of user’s tasks. But in the process of interface developing one can observe the same tendency as in general software evolution – they also become more and more complicated and insufficiently flexible. A solution of this problem might be intelligent interfaces, which are able to adapt themselves on user’s necessities, can learn, can take over the initiative in their interaction with user, are able to guide him in order to facilitate his objectives achieving in a fast and comfortable way.

Interface elaboration for Computer Algebra Systems (CAS) was and remains a topic to be investigated. There are a number of overviews treating this subject, among them we can mention (Kajler 1998, Kajler, Soiffer, 1998, Grabmeier, Kaltofen, Weispfenning, 2003). There is also a large list of concrete CAS descriptions, the corresponding interface (in a rudimentary or developed form) being one of the presented components.

Analysing the CAS interfaces one can conclude that they respect the requests specific for intelligent interfaces (Ross, 2000, Filip, 2007, Waern, 1997) in a reduced measure only:

  • The user is guided in the process of his problem solution,
  • It is not necessary to study preliminarily how the system works, often it is possible to learn the functioning mode directly in the process of interaction with the system,
  • The system makes some errors prevention (partially) by checking the problem’s formulation correctness and verification of initial data.

Less significant is the attention paid to user modelling. Because CAS are from the very beginning oriented on a pre-established users class, their model (in relation to the class of problems, data type, non-ambiguous interpreting) is beforehand included into the system.

Therefore in the case of CAS it would be more relevant to speak about user’s personalisation. We should mention that the problem of personalisation, being realised, is not sufficiently examined. User can apply some possibilities of individualisation, but he should adapt the system to his needs or preferences by himself.

We will note also that the possibility to interact in natural language in the frame of CAS is rather limited.

Thus, in order to increase the intelligence level of CAS interfaces one can fix the necessity to implement some additional features, including user’s personalisation, adaptation to his individual requests and preferences. One should note, that the user not always is able to formulate his preferences.

In the next sections the process of adaptation in CAS interfaces will be examined. We will respect the following principles:

  • The goal of adaptive systems is to facilitate user’s objectives achieving in the fastest and easiest way, assuring, as much as possible, a high degree of satisfaction (Ross, 2000).
  • It is important that system shall not irritate the user, not reduce by its interventions the speed of his work, it shall not initiate unbidden operations in some inappropriate moments, creating in such a way obstacles to achieve the goal immediately.

5. Conclusion

N. Kajler (Kajler, 1998) considers computer-human interaction in symbolic computation “a wide and interdisciplinary research area”, including different aspects, which “current user interfaces either do not handle well or do not address at all”. We tried to cover only a small part of them, namely the problem of adaptation to users using the mechanism of computational environments and sessions. Our experience shows that in many cases about 5 saved sessions are enough to be able to understand user’s preferences and to generate for him a convenient environment reflecting his domain of interests.


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