Showing papers presented at "Conference on Tools With Artificial Intelligence in 2000"

Using latent semantic analysis to identify similarities in source code to support program understanding

Abstract: The paper describes the results of applying Latent Semantic Analysis (LSA), an advanced information retrieval method, to program source code and associated documentation. Latent semantic analysis is a corpus based statistical method for inducing and representing aspects of the meanings of words and passages (of natural language) reflective in their usage. This methodology is assessed for application to the domain of software components (i.e., source code and its accompanying documentation). Here LSA is used as the basis to cluster software components. This clustering is used to assist in the understanding of a nontrivial software system, namely a version of Mosaic. Applying latent semantic analysis to the domain of source code and internal documentation for the support of program understanding is a new application of this method and a departure from the normal application domain of natural language.

An approach to incremental SVM learning algorithm

Abstract: The classification algorithm that is based on a support vector machine (SVM) is now attracting more attention, due to its perfect theoretical properties and good empirical results. In this paper, we first analyze the properties of the support vector (SV) set thoroughly, then introduce a new learning method, which extends the SVM classification algorithm to the incremental learning area. The theoretical basis of this algorithm is the classification equivalence of the SV set and the training set. In this algorithm, knowledge is accumulated in the process of incremental learning. In addition, unimportant samples are discarded optimally by a least-recently used (LRU) scheme. Theoretical analyses and experimental results showed that this algorithm could not only speed up the training process, but it could also reduce the storage costs, while the classification precision is also guaranteed.

Efficient defeasible reasoning systems

Abstract: For many years, the non-monotonic reasoning community has focussed on highly expressive logics. Such logics have turned out to be computationally expensive, and have given little support to the practical use of non-monotonic reasoning. In this work we discuss defeasible logic, a less-expressive but more efficient non-monotonic logic. We report on two new implemented systems for defeasible logic: a query answering system employing a backward chaining approach, and a forward-chaining implementation that computes all conclusions. Our experimental evaluation demonstrates that the systems can deal with large theories (up to hundreds of thousands of rules). We show that defeasible logic has linear complexity, which contrasts markedly with most other non-monotonic logics and helps to explain the impressive experimental results. We believe that defeasible logic, with its efficiency and simplicity is a good candidate to be used as a modelling language for practical applications, including modelling of regulations and business rules.

Multi-resolution on compressed sets of clauses

Abstract: The paper presents a system based on new operators for handling sets of propositional clauses represented by means of ZBDDs (zero-suppressed binary decision diagrams). The high compression power of such data structures allows efficient encodings of structured instances. A specialized operator for the distribution of sets of clauses is introduced and used for performing multi-resolution on clause sets. Cut eliminations between sets of clauses of exponential size may then be performed using polynomial size data structures. The ZREs system, a new implementation of the Davis-Putnam procedure (M. Davis and H. Putnam, 1960), solves two hard problems for resolution, that are currently out of the scope of the best SAT provers.

Model selection via meta-learning: a comparative study

Abstract: The selection of an appropriate inducer is crucial for performing effective classification. In previous work we presented a system called NOEMON which relied on a mapping between dataset characteristics and inducer performance to propose inducers for specific datasets. Instance based learning was used to create that mapping. Here we extend and refine the set of data characteristics; we also use a wider range of base-level inducers and a much larger collection of datasets to create the meta-models. We compare the performance of meta-models produced by instance based learners, decision trees and boosted decision trees. The results show that decision trees and boosted decision trees models enhance the perfomance of the system.

Meta-constraints on violations for over constrained problems

Abstract: Constraint programming techniques are widely used to solve real-world problems. It often happens that such problems are over-constrained and do not have any solution. In such a case, the goal is to find a good compromise. A simple theoretical framework is the Max-CSP, where the goal is to minimize the number of constraint violations. However, in real-life problems, complex rules are generally imposed with respect to violations. Solutions which do not satisfy these rules have no practical interest. Therefore, many frameworks derived from the Max-CSP have been introduced. In this paper, we classify the most usual types of rules, and we show that some of them are not expressible in existing frameworks. We introduce a new paradigm in which all these rules can be encoded, through meta-constraints. Moreover, we show that most of existing frameworks can be included in our model.

GA Tree: genetically evolved decision trees

Abstract: We use genetic algorithms to evolve classification decision trees. The performance of the system is measured on a set of standard discretized concept learning problems and compared (very favorably) with the performance of two known algorithms (C4.5, OneR).

Constrained genetic algorithms and their applications in nonlinear constrained optimization

Abstract: The paper presents a problem-independent framework that unifies various mechanisms for solving discrete constrained nonlinear programming (NLP) problems whose functions are not necessarily differentiable and continuous. The framework is based on the first-order necessary and sufficient conditions in the theory of discrete constrained optimization using Lagrange multipliers. It implements the search for discrete-neighborhood saddle points (SP/sub dn/) by performing ascents in the original-variable subspace and descents in the Lagrange-multiplier subspace. Our study on the various mechanisms shows that CSAGA, a combined constrained simulated annealing and genetic algorithm, performs well. Finally, we apply iterative deepening to determine the optimal number of generations in CSAGA.

Self-optimising CBR retrieval

Abstract: One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effort may be demanding. In this paper we present a tool which helps to reduce the knowledge acquisition effort for building a typical CBR retrieval stage consisting of a decision-tree index and similarity measure. We use genetic algorithms to determine the relevance/importance of case features and to find optimal retrieval parameters. The optimisation is done using the data contained in the case-base. Because no (or little) other knowledge is needed this results in a self-optimising CBR retrieval. To illustrate this we present how the tool has been applied to optimise retrieval for a tablet formulation problem.

A visualization tool for interactive learning of large decision trees

Abstract: Decision tree induction is certainly among the most applicable learning techniques due to its power and simplicity. However learning decision trees from large datasets, particularly in data mining, is quite different from learning from small or moderately sized datasets. When learning from large datasets, decision tree induction programs often produce very large trees. How to efficiently visualize trees in the learning process, particularly large trees, is still questionable and currently requires efficient tools. The paper presents a visualization tool for interactive learning of large decision trees, that includes a new visualization technique called T2.5D (Trees 2.5 Dimensions). After a brief discussion on requirements for tree visualizers and related work, the paper focuses on presenting developing techniques for two issues: (1) how to visualize efficiently large decision trees; and (2) how to visualize decision trees in the learning process.

History checking of temporal fuzzy logic formulas for monitoring behavior-based mobile robots

Abstract: Behavior-based robot control systems have shown remarkable success for controlling robots evolving in real world environments. However, they can fail in different manners due to their distributed control and their local decision making. In this case, monitoring can be used to detect failures and help to recover from them. In this work, we present an approach for specifying monitoring knowledge and a method for using this knowledge to detect failures. In particular we show how temporal fuzzy logic can be used to represent monitoring knowledge and then utilized to effectively detect runtime failures. New semantics are introduced to take into consideration uncertainty and noisy information. There are numbers of advantages to our approach including a declarative semantics for the monitoring knowledge and an independence of this knowledge from the implementation details of the control system. Moreover we show how our system can deal effectively with noisy information and sensor readings. Experiments with two real world robots and the simulator are used to illustrate failure examples and the benefits of failure detection and noise elimination.

A reactive method for real time dynamic vehicle routing problem

Abstract: The real time dynamic vehicle routing problem (RT-DVRP) is an extension of VRPTW, in which the problem parameters change in real time. We present a solution to RTDVRP: a concurrent, agent-based reactive vehicle routing system (RVRS) and the implementation of the RVRS, which combines a generic, concurrent infrastructure and a powerful incremental local optimization heuristic.

Fuzzy cellular automata: from theory to applications

Abstract: In this paper we present a fuzzified cellular automata structure called fuzzy cellular automata. We begin our paper with a fuzzified entity called fuzzy automaton, then we present basics of cellular automata and finally we define fuzzy cellular automata. At the end we present some simulation results from the field of fire spread in homogeneous nature environment.

Using Bayesian classifier in relevant feedback of image retrieval

Abstract: Relevance feedback is a powerful technique in content-based image retrieval (CBIR) and has been an active research area for the past few years. In this paper, we propose a new relevance feedback approach based on a Bayesian classifier, and it treats positive and negative feedback examples with different strategies. For positive examples, a Bayesian classifier is used to determine the distribution of the query space. A 'dibbling' process is applied to penalize images that are near the negative examples in the query and retrieval refinement process. The proposed algorithm also has a progressive learning capability that utilizes past feedback information to help the current query. Experimental results show that our algorithm is effective.

Modeling software quality: the Software Measurement Analysis and Reliability Toolkit

Abstract: The paper presents the Software Measurement Analysis and Reliability Toolkit (SMART) which is a research tool for software quality modeling using case based reasoning (CBR) and other modeling techniques. Modern software systems must have high reliability. Software quality models are tools for guiding reliability enhancement activities to high risk modules for maximum effectiveness and efficiency. A software quality model predicts a quality factor, such as the number of faults in a module, early in the life cycle in time for effective action. Software product and process metrics can be the basis for such fault predictions. Moreover, classification models can identify fault prone modules. CBR is an attractive modeling method based on automated reasoning processes. However, to our knowledge, few CBR systems for software quality modeling have been developed. SMART addresses this area. There are currently three types of models supported by SMART: classification based on CBR, CBR classification extended with cluster analysis, and module-order models, which predict the rank-order of modules according to a quality factor. An empirical case study of a military command, control, and communications applied SMART at the end of coding. The models built by SMART had a level of accuracy that could be very useful to software developers.

Reasoning about numeric and symbolic time information

Abstract: Many applications such as planning, scheduling and natural language processing involve managing both symbolic and numeric aspects of time. We have developed a temporal model, TemPro, based on interval algebra, to express such applications in terms of qualitative and quantitative temporal constraints. TemPro extends the interval algebra relations of Allen (1983) to handle numeric information. To solve a temporal constraint problem represented by TemPro, we have developed a method using constraint propagation at the numeric and symbolic levels. In order to deal with real time applications or those applications where a complete solution cannot be obtained, we have modified the propagation techniques so that they will be able to solve temporal problems by giving a solution with a quality depending on the time allocated for computation.

Principles for mining summaries using objective measures of interestingness

Abstract: An important problem in the area of data mining is the development of effective measures of interestingness for ranking discovered knowledge. The authors propose five principles that any measure must satisfy to be considered useful for ranking the interestingness of summaries generated from databases. We investigate the problem within the context of summarizing a single dataset which can be generalized in many different ways and to many levels of granularity. We perform a comparative sensitivity analysis of fifteen well-known diversity measures to identify those which satisfy the proposed principles. The fifteen diversity measures have previously been utilized in various disciplines, such as information theory, statistics, ecology, and economics. Their use as objective measures of interestingness for ranking summaries generated from databases is novel. The objective of this work is to gain some insight into the behaviour that can be expected from each of the diversity measures in practice, and to begin to develop a theory of interestingness against which the utility of new measures can be assessed.

Interpretation of self-organizing maps with fuzzy rules

Abstract: Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing maps (SOMs) can be used to map large data sets to a simpler; usually two-dimensional topological structure. This mapping is able to illustrate dependencies in the data in a very intuitive manner and allows fast location of clusters. However because of the black-box design of neural networks, it is difficult to get qualitative descriptions of the data. In our approach, we identify regions of interest in SOMs by using unsupervised clustering methods. Then we apply inductive learning methods to find fuzzy descriptions of these clusters. Through the combination of these methods, it is possible to use supervised machine learning methods to find simple and accurate linguistic descriptions of previously unknown clusters in the data.

A new statistical method for performance evaluation of search engines

Abstract: We present a new statistical method for evaluating search engines' precision performance based on sample queries. The method consists of relevance evaluation and statistical comparison. In relevance evaluation, we present two scoring algorithms: one is a term-based algorithm based on the vector space model, and the other is a new three-level algorithm modeled after manual methods commonly used in information retrieval studies. In statistical comparison, we apply a statistical metric probability of win, in ranking the search engines. Based on a set of sample queries, our method evaluates the relevance of the pages returned by the search engines and compares them statistically In the experiment, our method was applied to three search engines, AltaVista, Google, and InfoSeek, using two query sets derived from the domain of parallel and distributed processing. Our results show that the three-level scoring algorithm with a typical set of parameters obtained results consistent with those obtained using the manual method, whereas the term-based algorithm did not.

Parallel mining of association rules with a Hopfield type neural network

Abstract: Association rule mining (ARM) is one of the data mining problems receiving a great deal of attention in the database community. The main computation step in an ARM algorithm is frequent itemset discovery. In this paper, a frequent itemset discovery algorithm based on the Hopfield model is presented.

Cognitively adequate modelling of spatial reference in human-robot interaction

Abstract: The question addressed in this paper is which types of spatial reference human users employ in their interaction with a robot and how a cognitively adequate model of these strategies can be implemented. Experiments in human-robot interaction were carried out which show how human users approach an artificial communication partner which was designed on the basis of empirical findings regarding spatial references among humans. The results are considerable differences in the strategies which speakers employ to achieve spatial reference in human-robot interaction and in natural communication.

Object tracking and multimedia augmented transition network for video indexing and modeling

Abstract: S.C. Chen et al. (1999) proposed a multimedia augmented transition network (ATN) model, together with its multimedia input strings, to model and structure video data. This multimedia ATN model was based on an ATN model that had been used within the artificial intelligence (AI) arena for natural-language understanding systems, and its inputs were modeled by multimedia input strings. The temporal and spatial relations of semantic objects were captured by an unsupervised video segmentation method called the SPCPE (simultaneous partitioning and class parameter estimation) algorithm, and they were modeled by the multimedia input strings. However, the segmentation method used was not able to identify objects that are overlapped together within video frames. The identification of overlapped objects is a great challenge. For this purpose, a backtrack-chain-update-split algorithm is developed in this paper that identifies the split segment (object) and uses this information in the current frame to update the previous frames in a backtrack-chain manner. The proposed split algorithm provides more accurate temporal and spatial information of the semantic objects for video indexing.

Combining various algorithms to solve the ship berthing problem

Abstract: The Ship Berthing Problem (SBP) belongs to the category of NP-complete problems. In this paper we discuss the methods of representing the SBP with the use of a directed acyclic graph and the use of a acyclic list to represent valid solutions for the SBP. We then propose and investigate the performance of several variants of the randomized local search, Tabu search and genetic algorithm for solving the SBP.

Heuristics for the exam scheduling problem

Abstract: As part of the process of creating a campus-wide timetabling system for the National University of Singapore, the authors investigated examination-scheduling algorithms. The challenge in exam scheduling is to draw up the final examination timetable, taking into account a number of different constraints. The authors propose a different approach when the interexamination gaps (termed paper spread) of each student should be maximized. The results compare favorably against the actual timetable produced by the current manual system.

A genetic algorithm-based system for generating test programs for microprocessor IP cores

Abstract: The current digital systems design trend is quickly moving toward a design-and-reuse paradigm. In particular, intellectual property cores are becoming widely used. Since the cores are usually provided as encrypted gate-level netlist, they raise several testability problems. The authors propose an automatic approach targeting processor cores that, by resorting to genetic algorithms, computes a test program able to attain high fault coverage figures. Preliminary results are reported to assess the effectiveness of our approach with respect to a random approach.

The probably approximately correct (PAC) population size of a genetic algorithm

Abstract: Probably approximately correct learning, PAC-learning, is a framework for the study of learnability and learning machines. In this framework, learning is induced through a set of examples. The size of this set is such that with probability greater than 1-/spl delta/ the learning machine shows an approximately correct behavior with error no greater than /spl epsiv/. The authors use the PAC framework to derive the size of a GA population that with probability 1-/spl delta/ contains at least one individual /spl epsiv/-close to a target hypothesis or solution.

About the use of local consistency in solving CSPs

Abstract: Local consistency is often a suitable paradigm for solving constraint satisfaction problems. We show how search algorithms could be improved, thanks to a smart use of two filtering techniques (path consistency and singleton arc consistency). We propose a possible way to get benefits from using a partial form of path consistency (PC) during the search. We show how local treatment based on singleton arc consistency (SAC) can be used to achieve more powerful pruning.

Transforming supervised classifiers for feature extraction

Abstract: Supervised feature extraction is used in data classification and (unlike unsupervised feature extraction) it uses class labels to evaluate the quality of the extracted features. It can be computationally inefficient to perform exhaustive searches to find optimal subsets of features. This article proposes a supervised linear feature extraction algorithm based on the use of multivariate decision trees. The main motivation in proposing this new approach to feature extraction is to reduce the computation time required to induce new classifiers which are required to evaluate every new subset of features. The new feature extraction algorithm proposed uses an approach that is similar to the wrapper model method used in feature selection. In order to evaluate the performance of the proposed algorithm, several tests with real-world data have been performed. The fundamental importance of this new feature extraction method is found in its ability to significantly reduce the computational time required to extract features from large databases.

Vision based localisation for a mobile robot

Abstract: Presents a vision-based localisation system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially Markov decision process. The originality of this approach is the resulting data fusion process that uses both image matching and the decisions made by the robot in order to estimate the set of plausible positions of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.

What's new? Using prior models as a measure of novelty in knowledge discovery

Abstract: One of the challenges of knowledge discovery is identifying patterns that are interesting, with novelty an important component of interestingness. Another important aspect of knowledge discovery is making efficient use of background knowledge. This paper develops a definition of novelty relative to a prior model of the domain. The definition of novelty is tested using pneumonia outcome data and a prior model of pneumonia severity.