We present our research on acquiring domain knowledge related
to urban vehicular traffic by means of interaction with experts.
Such knowledge is needed in knowledge discovery and data mining for approximation
of complex vague concepts from the road traffic. According
to perception based computing paradigm, this can be done by construction
of hierarchical classifiers supported with expert knowledge. We treat
traffic, especially urban traffic, as a complex process having hierarchical
structure. Complexity of this process makes traffic data massive and complex,
what makes domain oriented hierarchical classifiers indispensable
here. We propose a method of traffic domain knowledge acquisition by
interaction with experts aimed at construction of such classifiers.

In the paper we propose an adaptive system for intelligent
traffic management in smart cities. We argue why the traffic is difficult
and complex phenomenon, why such traffic management systems are
necessary for the smart city, what is the state of the art in the traffic
science and traffic management and how to improve existing solutions
using methods that we develop, based on the Perception Based Computing
paradigm.

We present a methodology for improving the detection of
outlying Fire Service’s reports based on domain knowledge and dialogue
with Fire & Rescue domain experts. The outlying report is considered as
element which is significantly different from the remaining data. Outliers
are defined and searched on the basis of domain knowledge and dialogue
with experts. We face the problem of reducing high data dimensionality
without loosing specificity and real complexity of reported incidents. We
solve this problem by introducing a knowledge based generalization level
intermediating between analysed data and experts domain knowledge.
In the methodology we use the Formal Concept Analysis methods for
both generation appropriate categories from data and as tools supporting
communication with domain experts. We conducted two experiments in
finding two types of outliers in which outliers detection was supported
by domain experts.

In the paper, we outline our research on obtaining
domain knowledge related to vehicular traffic in cities using
interaction with experts. The goal of acquiring such knowledge
is to construct hierarchical domain oriented classifiers for
approximation of complex vague concepts related to the road
traffic. Interaction with experts in construction of hierarchical
classifiers is supported by the software for agent-based simulation
of vehicular traffic in cities, Traffic Simulation Framework,
developed by the first author.

Rough separability in topology is discussed by its connections
with pseudometric spaces and rough sets. Pseudometric spaces are
presented from the point of view of their connections with approximation
spaces. A special way of determining equivalence relations by pseudometric
spaces is considered and open sets in pseudometric spaces are studied.
Investigations focus on the class of pseudometric spaces which are lower
bounded in each point since open sets in these spaces coincide with definable
sets of some prescribed approximation spaces. It is also shown that
all equivalence and non transitive tolerance relations can be determined
by pseudometric spaces in specified ways.

Diverse attempts are being made to develop new computers, machines and systems that could act not only autonomously, but also in an increasingly intelligent, perceptual and cognitive manner. This paper discusses some of the educational challenges stemming from this emerging modelling and design paradigm, including teaching appropriate subjects to undergraduate and graduate students in university engineering programs.

The aim of this paper is to present a step toward building
computational models for interactive systems. Such computations
are performed in an integrated distributed environments on objects of
different kinds of complexity, called here as information granules. The
computations are progressing by interactions among information granules
and physical objects. We distinguish global and local computations.
The former ones are performed by the environment (the nature) while the
local computations are, in a sense, projections of the global computations
on local systems and they represent information on global computations
perceived by local systems. We assume that, the laws of the nature are
only partially known by the local systems. This approach seems to be
of some importance for developing computing models in different areas
such as natural computing (e.g., computing models for meta-heuristics
or computations models for complex processes in molecular biology),
computing in distributed environments under uncertainty realized by
multi-agent systems, modeling of computations for feature extraction
(constructive induction) for approximation of complex vague concepts,
hierarchical learning, discovery of planning strategies or strategies for
coalition formation by intelligent systems as well as for approximate reasoning
about interactive computations based on such computing models.
In the presented computing models, a mixture of reasoning based on deduction
and induction is used.

This article is focused on the recognition and prediction of blockages
in the fire stations using granular computing approach. Blockage refers to the
situation when all fire units are out and a new incident occurs. The core of the
method is an estimation of the expected return times for the fire brigades based
on the granularisation of source data. This estimation, along with some other
considerations allows for evaluation of the probability of the blockage.

We present a general scheme of interaction and we discuss the role of
interactions in modeling of perception processes. We use information systems as a
starting point for perception modeling, i.e., modeling of the process of understanding
of sensory measurements. The novelty of the paper is an attempt to present the
perception process by means of interactive grammars.

We discuss basic notions of Perception Based Computing
(PBC). Perception is characterized by sensory measurements and ability
to apply them to reason about satisfiability of complex vague concepts
used, e.g., as guards for actions or invariants to be preserved by agents.
Such reasoning is often referred as adaptive judgment. Vague concepts
can be approximated on the basis of sensory attributes rather than defined
exactly. Approximations usually need to be induced by using hierarchical
modeling. Computations require interactions between granules of
different complexity, such as elementary sensory granules, granules representing
components of agent states, or complex granules representing
classifiers that approximate concepts. We base our approach to interactive
computations on generalized information systems and rough sets.
We show that such systems can be used for modeling advanced forms of
interactions in hierarchical modeling. Unfortunately, discovery of structures
for hierarchical modeling is still a challenge. On the other hand, it
is often possible to acquire or approximate them from domain knowledge.
Given appropriate hierarchical structures, it becomes feasible to perform
adaptive judgment, starting from sensory measurements and ending with
conclusions about satisfiability degrees of vague target guards. Thus, our
main claim is that PBC should enable users (experts, researchers, students)
to submit domain knowledge, by means of a dialog. It should
be also possible to submit hypotheses about domain knowledge to be
checked semi-automatically. PBC should be designed more like laboratories
helping users in their research rather than fully automatic data
mining or knowledge discovery toolkit. In particular, further progress in
understanding visual perception – as a special area of PBC – will be
possible, if it becomes more open for cooperation with experts from neuroscience,
psychology or cognitive science. In general, we believe that
PBC will soon become necessity in many research areas.

This paper introduces a perceptual tolerance intersection of
sets as an example of near set operations. Such operations are motivated
by the need to consider similarities between digital images viewed as disjoint
sets of points. The proposed approach is in keeping with work by
E.C. Zeeman on tolerance spaces and visual perception and J.H. Poincar´e
on sets of similar sensations used to define representative (aka tolerance)
spaces such as visual, tactile and motile spaces. Perceptual tolerance
intersection of sets is a direct consequence of recent work on near sets and
a solution to the problem of how one goes about discovering
affinities between digital images. The main contribution of this article
is a description-based approach to assessing the resemblances between
digital images.

In this paper we discuss the importance of information systems
in modeling interactive computations performed on (complex) granules
and propose a formal approach to interactive computations based
on information systems. The basic concepts of information systems and
rough sets are interpreted in the framework of interactive computations.
We also show that information systems can be used for modeling more
advanced forms of interactions such as hierarchical ones. The role of
hierarchical interactions is emphasized in modeling interactive computations.
Some illustrative examples of interactions used in the hierarchical
multimodal classification method as well as in the ACT-R 6.0 system are
reported.

In the paper we discuss the importance of information systems in modelling interactive computations performed on (complex) granules and propose a formal approach to interactive computations based on information systems. The basic concepts of information systems and rough sets are interpreted in the framework of interactive computations. We also shows that information systems can be used for modeling more advanced forms of interactions such as hierarchical ones.  The role of hierarchical interactions is emphasized in modeling interactive computations. Some illustrative examples  of interactions used in the hierarchical multimodal classification method as well as in the ACT-R  system are reported.

A novel approach to extend the notions of definability and
rough set approximations in information systems with non-equivalence
relations is proposed. The upper approximation is defined as set-theoretic
complement of negative region of a given concept; therefore, it does not
need to be definable. Fundamental properties of new approximation operators
are compared with the previous ones reported in literature. The
proposed idea is illustrated within tolerance approximation spaces. In
particular, granulation based on maximal preclasses is considered.

The aim of this paper is to compare concept lattices and
approximation spaces. For this purpose general approximation spaces
are introduced. It is shown that formal contexts and information systems
on one hand and general approximation spaces on the other could
be mutually represented e.g. for every information system exists a general
approximation space such that both structures determines the same
indiscernibility relation. A close relationship between Pawlak’s approximation
spaces and general approximation spaces also holds: for each approximation
space exists a general approximation space such that both
spaces determine the same definable sets. It is shown on the basis of these
relationships that an extent of the every formal concept is a definable
set in some Pawlak’s approximation space. The problem when concept
lattices are isomorphic to algebras of definable sets in approximation
spaces is also investigated.

This paper is a preliminary step towards proposing a scheme for synthesis
of a concept out of a set of concepts focusing on the following aspects.
The first is that the semantics of a set of simple (or independent) concepts would
be understood in terms of its prototypes and counterexamples, where these instances
of positive and negative cases may vary with the change of the context,
i.e., a set of situations which works as a precursor of an information system. Secondly,
based on the classification of a concept in terms of the situations where
it strictly applies and where not, a degree of application of the concept to some
new situation/world would be determined. This layer of reasoning is named as
logic of prototypes and counterexamples. In the next layer the method of concept
synthesis would be designed as a graded concept based on the already developed
degree based approach for logic of prototypes and counterexamples.