Concept of inductive programming supporting anthropomorphic information technology

A new original approach to the formalization and implementation methods for the problem of inductive programming is described. This approach makes it possible for the first time to describe a wide range of problems within the given formalization based on examples from the implementation of problem-oriented languages to the development of applied systems with the help of these languages. Methods for passing on “procedure knowledge” that are accepted in information technologies and human communication are discussed and the concept of an “anthropomorphic information technology” is formulated. The general scheme for constructing this system based on the given technology is described. The fundamental role played by the mechanism of partial evaluation in providing the efficiency of implementation and maintenance of the extension mode for inductively specified languages is stressed. An example of inductive specification of a simple programming language and discuss the prospects of using the concept proposed is presented.     

On a confidence gain measure for association rule discovery and scoring

This article presents a new interestingness measure for association rules called confidence gain (CG). Focus is given to extraction of human associations rather than associations between market products. There are two main differences between the two (human and market associations). The first difference is the strong asymmetry of human associations (e.g., the association “shampoo” → “hair” is much stronger than “hair” → “shampoo”), where in market products asymmetry is less intuitive and less evident. The second is the background knowledge humans employ when presented with a stimulus (input phrase). CG calculates the local confidence of a given term compared to its average confidence throughout a given database. CG is found to outperform several association measures since it captures both the asymmetric notion of an association (as in the confidence measure) while adding the comparison to an expected confidence (as in the lift measure). The use of average confidence introduces the “background knowledge” notion into the CG measure. Various experiments have shown that CG and local confidence gain (a low-complexity version of CG) successfully generate association rules when compared to human free associations. The experiments include a large-scale “free sssociation Turing test” where human free associations were compared to associations generated by the CG and other association measures. Rules discovered by CG were found to be significantly better than those discovered by other measures. CG can be used for many purposes, such as personalization, sense disambiguation, query expansion, and improving classification performance of small item sets within large databases. Although CG was found to be useful for Internet data retrieval, results can be easily used over any type of database. Edited by J. Srivastava     

Compute‐intensive methods in artificial intelligence

In order to deal with the inherent combinatorial nature of many tasks in artificial intelligence, domain‐specific knowledge has been used to control search and reasoning or to eliminate the need for general inference altogether. However, the process of acquiring domain knowledge is an important bottleneck in the use of such “knowledge‐intensive” methods. Compute‐intensive methods, on the other hand, use extensive search and reasoning strategies to limit the need for detailed domain‐specific knowledge. The idea is to derive much of the needed information from a relatively compact formalization of the domain under consideration. Up until recently, such general reasoning strategies were much too expensive for use in applications of interesting size but recent advances in reasoning and search methods have shown that compute‐intensive methods provide a promising alternative to knowledge‐intensive methods. This revised version was published online in June 2006 with corrections to the Cover Date.     

Stable rankings for different effort models

There exists a large and growing number of proposed estimation methods but little conclusive evidence ranking one method over another. Prior effort estimation studies suffered from “conclusion instability”, where the rankings offered to different methods were not stable across (a) different evaluation criteria; (b) different data sources; or (c) different random selections of that data. This paper reports a study of 158 effort estimation methods on data sets based on COCOMO features. Four “best” methods were detected that were consistently better than the “rest” of the other 154 methods. These rankings of “best” and “rest” methods were stable across (a) three different evaluation criteria applied to (b) multiple data sets from two different sources that were (c) divided into hundreds of randomly selected subsets using four different random seeds. Hence, while there exists no single universal “best” effort estimation method, there appears to exist a small number (four) of most useful methods. This result both complicates and simplifies effort estimation research. The complication is that any future effort estimation analysis should be preceded by a “selection study” that finds the best local estimator. However, the simplification is that such a study need not be labor intensive, at least for COCOMO style data sets.     

The complexity of agent design problems: Determinism and history dependence

The agent design problem is as follows: given a specification of an environment, together with a specification of a task, is it possible to construct an agent that can be guaranteed to successfully accomplish the task in the environment? In this article, we study the computational complexity of the agent design problem for tasks that are of the form “achieve this state of affairs” or “maintain this state of affairs.” We consider three general formulations of these problems (in both non-deterministic and deterministic environments) that differ in the nature of what is viewed as an “acceptable” solution: in the least restrictive formulation, no limit is placed on the number of actions an agent is allowed to perform in attempting to meet the requirements of its specified task. We show that the resulting decision problems are intractable, in the sense that these are non-recursive (but recursively enumerable) for achievement tasks, and non-recursively enumerable for maintenance tasks. In the second formulation, the decision problem addresses the existence of agents that have satisfied their specified task within some given number of actions. Even in this more restrictive setting the resulting decision problems are either pspace-complete or np-complete. Our final formulation requires the environment to be history independent and bounded. In these cases polynomial time algorithms exist: for deterministic environments the decision problems are nl-complete; in non-deterministic environments, p-complete.     

Quantifiable data mining using ratio rules

Association Rule Mining algorithms operate on a data matrix (e.g., customers products) to derive association rules [AIS93b, SA96]. We propose a new paradigm, namely, Ratio Rules, which are quantifiable in that we can measure the “goodness” of a set of discovered rules. We also propose the “guessing error” as a measure of the “goodness”, that is, the root-mean-square error of the reconstructed values of the cells of the given matrix, when we pretend that they are unknown. Another contribution is a novel method to guess missing/hidden values from the Ratio Rules that our method derives. For example, if somebody bought $10 of milk and $3 of bread, our rules can “guess” the amount spent on butter. Thus, unlike association rules, Ratio Rules can perform a variety of important tasks such as forecasting, answering “what-if” scenarios, detecting outliers, and visualizing the data. Moreover, we show that we can compute Ratio Rules in a single pass over the data set with small memory requirements (a few small matrices), in contrast to association rule mining methods which require multiple passes and/or large memory. Experiments on several real data sets (e.g., basketball and baseball statistics, biological data) demonstrate that the proposed method: (a) leads to rules that make sense; (b) can find large itemsets in binary matrices, even in the presence of noise; and (c) consistently achieves a “guessing error” of up to 5 times less than using straightforward column averages. Received: March 15, 1999 / Accepted: November 1, 1999     

The sufficient and necessary condition for chance distribution of bifuzzy variable

Fuzzy sets and fuzzy variables have undergone several different extensions overtime. One of them involved including a “bifuzzy variable” as a fuzzy element for describing the more complete systems. The properties of bifuzzy variable were obtained by introducing the concept of “chance distribution”. In this paper, we will present a sufficient and necessary condition for chance distribution of bifuzzy variable. Here we present a constructive proof base on credibility theory for the sufficient part.     

Designing manipulative technologies for children with different abilities

We present a design approach for manipulative technologies that consider “user diversity” as a main lever for design. Different dimensions of “diversity” are considered, e.g., the users' age, abilities, culture, cultural background, and alphabetization. These dimensions drive the development of a user-centered design process for manipulative technologies for learning and play environments. In particular, we explore the possibility of allowing young children to develop and interact with virtual/physical worlds by manipulating physical objects in different contexts, like the classroom, the hospital, or the playground. In our scenarios, we consider children with different abilities (fully able, physically impaired, or with cognitive delays), in different cultures (Denmark, Tanzania, and Italy), and with a different level of alphabetization. The needs and expectations of such heterogeneous user-groups are taken into account through a user-centered design process to define a concept of tangible media for collaborative and distributed edutainment environments. The concept is implemented as a set of building blocks called I-Blocks with individual processing and communication power. Using the I-Blocks system, children can do “programming by building,” and thereby construct interacting artefacts in an intuitive manner without the need to learn and use traditional programming languages. Here, we describe in detail the technology of I-Blocks and discuss lessons learned from “designing for diversity.”     

Fuzzy linear regression model based on fuzzy scalar product

The new concept and method of imposing imprecise (fuzzy) input and output data upon the conventional linear regression model is proposed in this paper. We introduce the fuzzy scalar (inner) product to formulate the fuzzy linear regression model. In order to invoke the conventional approach of linear regression analysis for real-valued data, we transact the α-level linear regression models of the fuzzy linear regression model. We construct the membership functions of fuzzy least squares estimators via the form of “Resolution Identity” which is a well-known formula in fuzzy sets theory. In order to obtain the membership value of any given least squares estimate taken from the fuzzy least squares estimator, we transform the original problem into the optimization problems. We also provide two computational procedures to solve the optimization problems.     

Dynamic Root Rectangles Part Two: The Root-Two Rectangle and Design Applications

“Dynamic symmetry” is the name given by Jay Hambidge for the proportioning principle that appears in “root rectangles” where a single incommensurable ratio persists through endless spatial divisions. In Part One of a continuing series [Fletcher 2007], we explored the relative characteristics of root-two, -three, -four, and -five systems of proportion and became familiar with diagonals, reciprocals, complementary areas, and other components. In Part Two we consider the “application of areas” to root-two rectangles and other techniques for composing dynamic space plans.     

Beyond Mind: How Brains Make up Artificial Cognitive Systems

What I call semiotic brains are brains that make up a series of signs and that are engaged in making or manifesting or reacting to a series of signs: through this semiotic activity they are at the same time engaged in “being minds” and so in thinking intelligently. An important effect of this semiotic activity of brains is a continuous process of disembodiment of mind that exhibits a new cognitive perspective on the mechanisms underling the semiotic emergence of meaning processes. Indeed at the roots of sophisticated thinking abilities there is a process of disembodiment of mind that presents a new cognitive perspective on the role of external models, representations, and various semiotic materials. Taking advantage of Turing’s comparison between “unorganized” brains and “logical” and “practical” machines” this paper illustrates the centrality to cognition of the disembodiment of mind from the point of view of the interplay between internal and external representations, both mimetic and creative. The last part of the paper describes the concept of mimetic mind I have introduced to shed new cognitive and philosophical light on the role of computational modeling and on the decline of the so-called Cartesian computationalism.     

Detecting Irregularities in Images and in Video

We address the problem of detecting irregularities in visual data, e.g., detecting suspicious behaviors in video sequences, or identifying salient patterns in images. The term “irregular” depends on the context in which the “regular” or “valid” are defined. Yet, it is not realistic to expect explicit definition of all possible valid configurations for a given context. We pose the problem of determining the validity of visual data as a process of constructing a puzzle: We try to compose a new observed image region or a new video segment (“the query”) using chunks of data (“pieces of puzzle”) extracted from previous visual examples (“the database”). Regions in the observed data which can be composed using large contiguous chunks of data from the database are considered very likely, whereas regions in the observed data which cannot be composed from the database (or can be composed, but only using small fragmented pieces) are regarded as unlikely/suspicious. The problem is posed as an inference process in a probabilistic graphical model. We show applications of this approach to identifying saliency in images and video, for detecting suspicious behaviors and for automatic visual inspection for quality assurance. Patent Pending     

Efficient Discovery of Unusual Patterns in Time Series

The problem of finding a specified pattern in a time series database (i.e., query by content) has received much attention and is now a relatively mature field. In contrast, the important problem of enumerating all surprising or interesting patterns has received far less attention. This problem requires a meaningful definition of “surprise”, and an efficient search technique. All previous attempts at finding surprising patterns in time series use a very limited notion of surprise, and/or do not scale to massive datasets. To overcome these limitations we propose a novel technique that defines a pattern surprising if the frequency of its occurrence differs substantially from that expected by chance, given some previously seen data. This notion has the advantage of not requiring the user to explicitly define what is a surprising pattern, which may be hard, or perhaps impossible, to elicit from a domain expert. Instead, the user gives the algorithm a collection of previously observed “normal” data. Our algorithm uses a suffix tree to efficiently encode the frequency of all observed patterns and allows a Markov model to predict the expected frequency of previously unobserved patterns. Once the suffix tree has been constructed, a measure of surprise for all the patterns in a new database can be determined in time and space linear in the size of the database. We demonstrate the utility of our approach with an extensive experimental evaluation.     

Discovering Dynamic Characteristics of Biochemical Pathways using Geometric Patterns among Parameter-Parameter Dependencies in Differential Equations

This paper proposes a novel approach to the analysis and validation of mathematical models using two-dimensional geometrical patterns representing parameter-parameter dependencies (PPD) in dynamic systems. A geometrical pattern is obtained by calculating moment values, such as the area under the curve (AUC), area under the moment curve (AUMC), and mean residence time (MRT), for a series of simulations with a wide range of parameter values. In a mathematical model of the metabolic pathways of the cancer drug irinotecan (CPT11), geometrical patterns can be classified into three major categories: “independent,” “hyperbolic,” and “complex.” These categories characterize substructures arising in differential equations, and are helpful for understanding the behavior of large-scale mathematical models. The Open Bioinformatics Grid (OBIGrid) provides a cyber-infrastructure for users to share these data as well as computational resources.     

Calculation of the number of complete mappings for permutations

The computation time for counting “good” permutations rapidly grows as the length of permutations increases. The paper presents algorithms for enumeration of “good” permutations. Algorithms reducing twice the number of “good” permutations that should be counted are considered along with the algorithm employing the concept of weight of a “good” permutation. Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 106–110, March–April, 2000.     

Management: a scientific discipline for humanity

This paper proposed a new concept of management: Managing According to Reason (MR). Since “manage” means to lead, plan, organize and control, and “reason” means to understand the law of the development of objects being managed, this new concept is an integration of the two elements of “managing” and “reason”. MR studies the contradictory relationship between “managing” and “reason”, and considers how such a relationship changes and develops. MR is an integration of the disciplines of management, philosophy, natural science, engineering technology, and social science. We believe the MR is a comprehensive scientific discipline that will greatly benefit humanity. Since “management” cannot work without power, while “reason” relies on science, MR is an integration of power and science. Power is MR’s assurance, and science is MR’s basis. We believe that MR will play a major role in twenty-first century.     

A notion of task relatedness yielding provable multiple-task learning guarantees

The approach of learning multiple “related” tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow “algorithmically related”, in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We offer an alternative approach, defining relatedness of tasks on the basis of similarity between the example generating distributions that underlie these tasks. We provide a formal framework for this notion of task relatedness, which captures a sub-domain of the wide scope of issues in which one may apply a multiple task learning approach. Our notion of task similarity is relevant to a variety of real life multitask learning scenarios and allows the formal derivation of generalization bounds that are strictly stronger than the previously known bounds for both the learning-to-learn and the multitask learning scenarios. We give precise conditions under which our bounds guarantee generalization on the basis of smaller sample sizes than the standard single-task approach. Editors: Daniel Silver, Kristin Bennett, Richard Caruana. A preliminary version of this paper appears in the proceedings of COLT’03, (Ben-David and Schuller 2003).     

Maintaining bounded-size sample synopses of evolving datasets

Perhaps the most flexible synopsis of a database is a uniform random sample of the data; such samples are widely used to speed up processing of analytic queries and data-mining tasks, enhance query optimization, and facilitate information integration. The ability to bound the maximum size of a sample can be very convenient from a system-design point of view, because the task of memory management is simplified, especially when many samples are maintained simultaneously. In this paper, we study methods for incrementally maintaining a bounded-size uniform random sample of the items in a dataset in the presence of an arbitrary sequence of insertions and deletions. For “stable” datasets whose size remains roughly constant over time, we provide a novel sampling scheme, called “random pairing” (RP), that maintains a bounded-size uniform sample by using newly inserted data items to compensate for previous deletions. The RP algorithm is the first extension of the 45-year-old reservoir sampling algorithm to handle deletions; RP reduces to the “passive” algorithm of Babcock et al. when the insertions and deletions correspond to a moving window over a data stream. Experiments show that, when dataset-size fluctuations over time are not too extreme, RP is the algorithm of choice with respect to speed and sample-size stability. For “growing” datasets, we consider algorithms for periodically resizing a bounded-size random sample upwards. We prove that any such algorithm cannot avoid accessing the base data, and provide a novel resizing algorithm that minimizes the time needed to increase the sample size. We also show how to merge uniform samples from disjoint datasets to obtain a uniform sample of the union of the datasets; the merged sample can be incrementally maintained. Our new RPMerge algorithm extends the HRMerge algorithm of Brown and Haas to effectively deal with deletions, thereby facilitating efficient parallel sampling.     

Structural optimization for post-buckling behavior using particle swarms

The aim of this paper is to develop a new algorithm based on the particle swarm optimization (PSO) concept and then to apply it in the solution of some new structural optimization problems for post-buckling behavior. Proposed modifications of the algorithm regard both the PSO kernel and the constraints handling. The “controlled reflection” technique is proposed for dealing with inequality constraints. The values of the objective are calculated for some control points chosen along a move vector. The position for which the objective is the smallest one and the constraints are not violated is selected. For the case of equality constraints, the “particle trap” strategy is proposed. First, equalities are transformed into inequalities forming constraint “zone of influence.” If a particle from a swarm drops into this “zone” it remains trapped there and can move further only inside this subspace. Simultaneously, a penalty term is added to the objective function to force particles to be “captured” and constraints to become active at the optimum. The new PSO algorithm has been successfully applied to problems of structural optimization against instability. The standard maximization of the critical load is performed both for single and double buckling loads. The modified optimization for post-buckling behavior is also performed. A new problem of reconstruction of a predicted post-buckling path is formulated. The sum of squared distances between the control points of a given equilibrium path and the reconstructed one is minimized. Another new problem regards the modification of the slope of nonlinear equilibrium curve. This is obtained by adding a set of post-buckling constraints imposed on derivative values calculated for selected control points at the equilibrium curve.This is the full version of the paper presented at the Congress WCSMO6, Rio de Janeiro, 2005.     

Simulation analysis of decision-making policy for on-demand transport systems

In recent years, on-demand transport systems (such as a demand-bus system) are focused as a new transport service in Japan. An on-demand vehicle visits pick-up and delivery points by door-to-door according to the occurrences of requests. This service can be regarded as a cooperative (or competitive) profit problem among transport vehicles. Thus, a decision-making for the problem is an important factor for the profits of vehicles (i.e., drivers). However, it is difficult to find an optimal solution of the problem, because there are some uncertain risks, e.g., the occurrence probability of requests and the selfishness of other rival vehicles. Therefore, this paper proposes a transport policy for on-demand vehicles to control the uncertain risks. First, we classify the profit of vehicles as “assured profit” and “potential profit”. Second, we propose a “profit policy” and “selection policy” based on the classification of the profits. Moreover, the selection policy can be classified into “greed”, “mixed”, “competitive”, and “cooperative”. These selection policies are represented by selection probabilities of the next visit points to cooperate or compete with other vehicles. Finally, we report simulation results and analyze the effectiveness of our proposal policies.