82,046 research outputs found
MODELLING EXPECTATIONS WITH GENEFER- AN ARTIFICIAL INTELLIGENCE APPROACH
Economic modelling of financial markets means to model highly complex systems in which expectations can be the dominant driving forces. Therefore it is necessary to focus on how agents form their expectations. We believe that they look for patterns, hypothesize, try, make mistakes, learn and adapt. AgentsÆ bounded rationality leads us to a rule-based approach which we model using Fuzzy Rule-Bases. E. g. if a single agent believes the exchange rate is determined by a set of possible inputs and is asked to put their relationship in words his answer will probably reveal a fuzzy nature like: "IF the inflation rate in the EURO-Zone is low and the GDP growth rate is larger than in the US THEN the EURO will rise against the USD". æLowÆ and ælargerÆ are fuzzy terms which give a gradual linguistic meaning to crisp intervalls in the respective universes of discourse. In order to learn a Fuzzy Fuzzy Rule base from examples we introduce Genetic Algorithms and Artificial Neural Networks as learning operators. These examples can either be empirical data or originate from an economic simulation model. The software GENEFER (GEnetic NEural Fuzzy ExplorER) has been developed for designing such a Fuzzy Rule Base. The design process is modular and comprises Input Identification, Fuzzification, Rule-Base Generating and Rule-Base Tuning. The two latter steps make use of genetic and neural learning algorithms for optimizing the Fuzzy Rule-Base.
Deterministic Polynomial-Time Algorithms for Designing Short DNA Words
Designing short DNA words is a problem of constructing a set (i.e., code) of
n DNA strings (i.e., words) with the minimum length such that the Hamming
distance between each pair of words is at least k and the n words satisfy a set
of additional constraints. This problem has applications in, e.g., DNA
self-assembly and DNA arrays. Previous works include those that extended
results from coding theory to obtain bounds on code and word sizes for
biologically motivated constraints and those that applied heuristic local
searches, genetic algorithms, and randomized algorithms. In particular, Kao,
Sanghi, and Schweller (2009) developed polynomial-time randomized algorithms to
construct n DNA words of length within a multiplicative constant of the
smallest possible word length (e.g., 9 max{log n, k}) that satisfy various sets
of constraints with high probability. In this paper, we give deterministic
polynomial-time algorithms to construct DNA words based on derandomization
techniques. Our algorithms can construct n DNA words of shorter length (e.g.,
2.1 log n + 6.28 k) and satisfy the same sets of constraints as the words
constructed by the algorithms of Kao et al. Furthermore, we extend these new
algorithms to construct words that satisfy a larger set of constraints for
which the algorithms of Kao et al. do not work.Comment: 27 page
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Society-in-the-Loop: Programming the Algorithmic Social Contract
Recent rapid advances in Artificial Intelligence (AI) and Machine Learning
have raised many questions about the regulatory and governance mechanisms for
autonomous machines. Many commentators, scholars, and policy-makers now call
for ensuring that algorithms governing our lives are transparent, fair, and
accountable. Here, I propose a conceptual framework for the regulation of AI
and algorithmic systems. I argue that we need tools to program, debug and
maintain an algorithmic social contract, a pact between various human
stakeholders, mediated by machines. To achieve this, we can adapt the concept
of human-in-the-loop (HITL) from the fields of modeling and simulation, and
interactive machine learning. In particular, I propose an agenda I call
society-in-the-loop (SITL), which combines the HITL control paradigm with
mechanisms for negotiating the values of various stakeholders affected by AI
systems, and monitoring compliance with the agreement. In short, `SITL = HITL +
Social Contract.'Comment: (in press), Ethics of Information Technology, 201
myCopter: Enabling Technologies for Personal Aerial Transportation Systems: Project status after 2.5 years
Current means of transportation for daily commuting are reaching their limits during peak travel times, which results in waste of fuel and loss of time and money. A recent study commissioned by the European Union considers a personal aerial transportation system (PATS) as a viable alternative for transportation to and from work. It also acknowledges that developing such a transportation system should not focus on designing a new flying vehicle for personal use, but instead on investigating issues surrounding the implementation of the transportation system itself. This is the aim of European project myCopter: to determine the social and technological aspects needed to set up a transportation system based on personal aerial vehicles (PAVs). The project focuses on three research areas: human-machine interfaces and training, automation technologies, and social acceptance. Our extended abstract for inclusion in the conference proceedings and our presentation will focus on the achievements during the first 2.5 years of the 4-year project. These include the development of an augmented dynamic model of a PAV with excellent handling qualities that are suitable for training purposes. The training requirements for novice pilots are currently under development. Experimental evaluations on haptic guidance and human-in-the-loop control tasks have allowed us to start implementing a haptic Highway-in-the-Sky display to support novice pilots and to investigate metrics for objectively determining workload using psychophysiological measurements. Within the project, developments for automation technologies have focused on vision-based algorithms. We have integrated such algorithms in the control and navigation architecture of unmanned aerial vehicles (UAVs). Detecting suitable landing spots from monocular camera images recorded in flight has proven to reliably work off-line, but further work is required to be able to use this approach in real time. Furthermore, we have built multiple low-cost UAVs and equipped them with radar sensors to test collision avoidance strategies in real flight. Such algorithms are currently under development and will take inspiration from crowd simulations. Finally, using technology assessment methodologies, we have assessed potential markets for PAVs and challenges for its integration into the current transportation system. This will lead to structured discussions on expectations and requirements of potential PAV users
Approximation algorithms for planning and control
A control system operating in a complex environment will encounter a variety of different situations, with varying amounts of time available to respond to critical events. Ideally, such a control system will do the best possible with the time available. In other words, its responses should approximate those that would result from having unlimited time for computation, where the degree of the approximation depends on the amount of time it actually has. There exist approximation algorithms for a wide variety of problems. Unfortunately, the solution to any reasonably complex control problem will require solving several computationally intensive problems. Algorithms for successive approximation are a subclass of the class of anytime algorithms, algorithms that return answers for any amount of computation time, where the answers improve as more time is allotted. An architecture is described for allocating computation time to a set of anytime algorithms, based on expectations regarding the value of the answers they return. The architecture described is quite general, producing optimal schedules for a set of algorithms under widely varying conditions
Clustering as an example of optimizing arbitrarily chosen objective functions
This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison
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