کلمات کلیدی مربوط به کتاب نقشه های شناختی: انفورماتیک و مهندسی کامپیوتر، هوش مصنوعی، پایگاه های دانش و سیستم های خبره
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Издательство InTech, 2010, -148 pp.
Cognitive maps have emerged as an
important tool in modeling and decision making. In a nutshell
they are signed di-graphs that capture the cause/effect
relationships that subject matter experts believe exist in a
problem space under consideration. Each node in the map
represents some variable concept. These generally fall into one
of several hard categories: physical attributes of the
environment, characteristics of artifacts embedded in the
problem space, or one of several soft areas: decisions being
made, social, psychological or cultural characteristics of the
decision makers, intentions, etc. Part of the value of
cognitive maps is that these hard and soft concepts can be
seamlessly mixed in them to build a more robust model of the
problem.
Edges in the map connect nodes for which a causal relationship
is believed to exist. The edge is directed from the causal node
to the effect node. In a general cognitive map, the edges have
integer strengths of 1, indicating direct causality, -1,
indicating inverse causality, and 0, indicating no causal link.
A special type of cognitive maps, a fuzzy cognitive map, allows
fuzziness in the modeling of the edge strengths. Unlike nodes
that have crisp values, edge strengths can have any fractional
value on the interval [-1,1], with fractional values indicating
partial causality. Thus, relationships such as A somewhat
affects B, or A really causes B can be captured and
incorporated in the map. The ability to model partial causality
in the map gives this technique great value in problem spaces
that have complex interactions between the physical
environment, man-made machines and decisions by human
operators.
The map is a true model in the sense that it has predictive
capabilities. In a typical situation, a set of nodes with known
values are designated inputs. These values are applied to the
map and held constant at their known values. In much the same
way that voltage or current sources are sources of energy in an
electrical circuit, these input nodes represent sources of
causality in the map. These input values are then propagated
through the map, using a user defined thresholding function at
each node to map its inputs to one of the permissible nodal
values. The process is repeated multiple times for all nodes in
the map until one of two meta-situations develops. Either the
map will reach equilibrium in the sense that the nodal values
remain constant, or it will reach a limit cycle, an oscillatory
condition where a group of nodes change back and forth between
two more sets of values.
Unlike a state machine, the actual sequence that the causal
values work through the map are generally unimportant. The map
itself represents a systems-level model. As such the one must
view all nodal values in the map as contributing to an
understanding of its behavior. Thus, system behavior is
indicated by the totality of nodal values present after the
input values have been applied and propagated through it to
equilibrium.
Cognitive maps have several distinct advantages over other
decision making tools like decision trees or Petri nets. First,
because states of nodes are compared to states of nodes, a
common numeric metric is not necessary for all values. Instead
changes in the underlying concepts of nodes are compared to
changes to the underlying concepts in other nodes. Thus,
cognitive maps truly allow apples to be compared to oranges.
Second, feedback is allowed in the map. With feedback, effects
can be mitigated (negative feedback) or reinforced (positive
feedback) by certain causes. Finally, the map can be pieced
together from many smaller maps through common nodes. Subject
matter experts can then provide a model within their domain of
expertise, without the need to understand to any great detail
the relevant concepts from other areas of interest. Each
subject matter expert can then focus on what they know best
contributing to the development of a robust systems model.
Interestingly, feedback and feedforward loops often appear when
the individual maps are pieced together that are not present
individually. This may explain why unseen problems can develop
in a system even though each subsystem is thoroughly tested and
validated.
Completed cognitive maps are be used in two general ways:
decision assessment and system diagnosis. In decision
assessment a set of known values are applied to input nodes in
the map and allowed to propagate to equilibrium with an eye to
the nodal values that result. The goal with this process is to
assess what changes in the state of the system can be expected
given a set of initial conditions. Part of the value of this
technique is that the input nodes are not fixed. They can
change depending on the context and the available
information.
In system diagnosis, one is more interested in what inputs give
rise to a system state of interest. The map affords ways to
make this determination. Since they can be become very complex
quickly as nodes are added, the topology of the model itself
can be used as a diagnostic tool. Using basic matrix techniques
nodes and combinations of nodes can be identified that are
linked to the output nodes of interest through some, possibly
lengthy, chain of cause-effect relationships in the map. By
identifying sets of these causal nodes, input values can then
be applied to them to see if the system state under examination
results.
The chapters in this book cover a spectrum of insights into the
development, use and applications of cognitive mapping and
fuzzy cognitive mapping techniques.
Topic Maps as Indexing Tools in the
Educational Sphere: Theoretical Foundations, Review of
Empirical Research and Future Challenges
A Cognitive Approach for Performance Measurement in Flexible
Manufacturing Systems using Cognitive Maps
System Diagnosis Using Fuzzy Cognitive Maps
Subject-formal Methods Based on Cognitive Maps and the Problem
of Risk Due to the Human Factor
From Physical Brain to Social Brain
The Role of Public Visual Art in Urban Space Recognition
The Representation of Objects in the Brain, and Its Link with
Semantic Memory and Language: a Conceptual Theory with the
Support of a Neurocomputational Model
Genetics of Cognition-What can Developmental Disorders Teach
Us?