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Wiki Page: Exploratory data analysis by genetic feature extraction
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Title: Exploratory data analysis by genetic feature extraction
Description: Understanding the waves in EEG signals is an hard task and psicologists often need automatic tools to perform this task. In this project we are interested in using a genetic algorithm developed for P300 feature extraction in order to extract useful informations from Error Potentials. The project is a collaboration with the psicology department od Padua University.
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: BioSignal Analysis
Research Topic: Brain-Computer Interface

Wiki Page: Extended Kalman Filtering on Manifolds
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Title: Extended Kalman Filtering on Manifolds
Description: Extended Kalman filtering is a well known technique for the estimation of the state of a dynamical system also used in robotics for localization and mapping. However in the basic formulation it assumes all variables to live in an Euclidean space while some components may span over the non-Euclidean 2D or 3D rotation group SO(2) or SO(3). It is thus possible to write tha Extended Kalman filter to operate on Lie Groups to take into account the presence of manifolds (http://www.ethaneade.org/latex2html/lie/lie.html). We are interestend in investigation this further applying it to the EKF-SLAM framework we have developed.

Material:

  • papers about Manifold based optimization and space representations
  • C++ framework for EKF-SLAM

Expected outcome:

  • An extended Kalman filter which uses this new representation

Required skills or skills to be acquired:

  • Good mathematical background
  • C++ programming under Linux
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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), [[DavideCucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2012
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: None

Wiki Page: Extending a search engine with semantic information
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Title: Extending a search engine with semantic information
Description: We are used to keyword-based search engines, where only documents matching the exact words in the query are retrieved. In a traditional search engine, if you submit the query "a dinosaur in a university in Lombardy" you won't probably find a document containing the phrase "a velociraptor in Politecnico di Milano", even though it's more or less what you were looking for.

Aim of this project is to expand a traditional search engine with semantic information, so that also documents containing words related to the ones in the query can be retrieved. Existing thesauri and ontologies can be used, as well as more dynamic and collaborative sources of knowledge such as user tags and wikipedia pages and categories.

Starting points for this work can be the projects "SeQuEx - Semantic Query Expansion" and "Enriching search results with semantic metadata".

Tutor: [[DavidLaniado | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Search

Wiki Page: Human-Like AI in Games
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Title: Human-Like AI in Games
Description: Developing a human-like AI is a challenging and fascinating problem from the point of view of the Artificial Intelligence research. At the same time, it is also a significative prolem for the computer games development: playing against humans is generally more exciting than playing against computers.

Our projects and theses on this topic involve two different games: Unreal Tournament 2004 and TORCS. Please contact us for additional information.

References
Tutor: [[DanieleLoiacono | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Computational Intelligence and Games
Research Topic: Computational Intelligence and Games

Wiki Page: Information geometry and machine learning
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Title: Information geometry and machine learning
Description: In machine learning, we often introduce probabilistic models to handle uncertainty in the data, and most of the times due to the computational cost, we end up selecting (a priori, or even at run time) a subset of all possible statistical models for the variables that appear in the problem. From a geometrical point of view, we work with a subset (of points) of all possible statistical models, and the choice of the fittest model in out subset can be interpreted as a the point (distribution) minimizing some distance or divergence function w.r.t. the true distribution from which the observed data are sampled. From this perspective, for instance, estimation procedures can be considered as projections on the statistical model and other statistical properties of the model can be understood in geometrical terms. Information Geometry (1,2) can be described as the study of statistical properties of families of probability distributions, i.e., statistical models, by means of differential and Riemannian geometry.

Information Geometry has been recently applied in different fields, both to provide a geometrical interpretation of existing algorithms, and more recently, in some contexts, to propose new techniques to generalize or improve existing approaches. Once the student is familiar with the theory of Information Geometry, the aim of the project is to apply these notions to existing machine learning algorithms.

Possible ideas are the study of a particular model from the point of view of Information Geometry, for example as Hidden Markov Models, Dynamic Bayesian Networks, or Gaussian Processes, to understand if Information Geometry can give useful insights with such models. Other possible direction of research include the use of notions and ideas from Information Geometry, such as the mixed parametrization based on natural and expectation parameters (3) and/or families of divergence functions (2), in order to study model selection from a geometric perspective. For example by exploiting projections and other geometric quantities with "statistical meaning" in a statistical manifold in order to chose/build the model to use for inference purposes.

Since the project has a theoretical flavor, mathematical inclined students are encouraged to apply. The project requires some extra effort in order to build and consolidate some background in math, partially in differential geometry, and especially in probability and statistics.

Bibliography

  1. Shun-ichi Amari, Hiroshi Nagaoka, Methods of Information Geometry, 2000
  2. Shun-ichi Amari, Information geometry of its applications: Convex function and dually flat manifold, Emerging Trends in Visual Computing (Frank Nielsen, ed.), Lecture Notes in Computer Science, vol. 5416, Springer, 2009, pp. 75–102
  3. Shun-ichi Amari, Information geometry on hierarchy of probability distributions, IEEE Transactions on Information Theory 47 (2001), no. 5, 1701–1711.
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 20 - 20
Research Area: Machine Learning
Research Topic: Information Geometry

Wiki Page: LARS and LASSO in non Euclidean Spaces
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Title: LARS and LASSO in non Euclidean Spaces
Description: LASSO (1) and more recently LARS (2) are two algorithms proposed for linear regression tasks. In particular LASSO solves a least-squares (quadratic) optimization problem with a constrain that limits the sum of the absolute value of the coefficients of the regression, while LARS can be considered as a generalization of LASSO, that provides a more computational efficient way to obtain the solution of the regression problem simultaneously for all values of the constraint introduced by LASSO.

One of the common hypothesis in regression analysis is that the noise introduced in order to model the linear relationship between regressors and dependent variable has a Gaussian distribution. A generalization of this hypothesis leads to a more general framework, where the geometry of the regression task is no more Euclidean. In this context different estimation criteria, such as maximum likelihood estimation and other canonical divergence functions do not coincide anymore. The target of the project is to compare the different solutions associated to different criteria, for example in terms of robustness, and propose generalization of LASSO and LARS in non Euclidean contexts.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the project has also a theoretical flavor, mathematical inclined students are encouraged to apply. The project may require some extra effort in order to build and consolidate some background in math, especially in probability and statistics.

Picture taken from (2)

Bibliography

  1. Tibshirani, R. (1996), Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58, No. 1, pages 267-288
  2. Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani, Least Angle Regression, 2003
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 20 - 20
Research Area: Machine Learning
Research Topic: Informtion Geometry

Wiki Page: LCM middleware on embedded platform
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Title: LCM middleware on embedded platform
Description: We are developing a framework for rapid prototyping of low-cost robotic systems. To fasten robot design and building, and to make software and hardware reuse easier, a modular architecture is mandatory.

In a context of smart modules that have to cooperate by exchanging data to reach their common goal, the communication protocol and middleware are core components. This project is about the middleware component, a publish/subscribe system that takes care of managing topics, publisher and subscribers, and of marshaling data before sending it. This project aims at porting the LCM marshaling and middleware library, developed at MIT and used in the Grand Challenge competition, to embedded systems, in order to exploit the existing LCM tools and to be compliant with an existing and efficient technology.

The project consists in:

  • stripping non necessary features of LCM to match the constraints of an embedded system and of the communication protocol
  • adding necessary features, like the concept of deadline (and priority as a consequence), that are mandatory for a real time distributed system
  • building a gateway, on an embedded platform, that acts as gateway between the standard-LCM world and the embededd-LCM network

The projects has to be developed in ANSI C, and experience with embedded platforms is a plus.

Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2011
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: Robot development

Wiki Page: Mesh Refinement with Deep Learning
Title: Mesh Refinement with Deep Learning
Description: Mesh Refinement with Deep Learning
Tutor: [[AndreaRomanoni | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: October 2018
Students: 1
CFU: 5
Research Area: Computer Vision and Image Analysis

Wiki Page: Mining wikipedia categories
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Title: Wikipedia category map
Description: Wikipedia articles are organized in a hierarchy of categories, manually assigned by users. This process can be considered a huge effort for the collective categorization of human knowledge; the result is a wide and disordered graph which can provide precious information for a variety of applications (natural language processing, information retrieval, ontology building...).

In the project "Wikipedia Category Map" a tool has been developed to extract the graph of Wikipedia categories, to store it in RDF format and to interactively visualize and explore it. Aim of this project is to analyze the resulting graph for the extraction of semantic relationships; for example it is possible to define metrics of distance between topics in the graph, which can be useful for various purposes in information retrieval.

Tutor: [[DavidLaniado | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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), [[RiccardoTasso | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 7 July 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Tagging

Wiki Page: MoonSLAM Reengineering
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Title: Reengineering of a flexible framework for simultaneous localization and mapping
Description: In the last three years a general framework for the implementation of EKF-SLAM algorithm has been developed at the AIRLab. After several improvements it is now time to redesign it based on the experience cumulated. The goal is to have an international reference framework for the development of EKF based SLAM algorithms with multiple sensors (e.g., lasers, odometers, inertial measurement ) and different motion models (e.g., free 6DoF motion, planar motion, ackerman kinematic, and do on). The basic idea is to implement it by using C++ templates, numerically stable techniques for Kalman filtering and investigation the use of automatic differentiation. It should be possible to seamlessly exchange motion model and sensor model without having to write code beside the motion model and the measurement equation.

Material

  • lots of theoretical background and material
  • an existing (and working) C++ implementation of the framework

Expected outcome:

  • a C++ library for the implementation of generic EKF-SLAM algorithms

Required skills:

  • Experienced C++ programming under Linux
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: None

Wiki Page: Odometric system based on circular points
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Title: An odometric sensor based on circular points
Description: Development of an odometric sensor based on an uncalibrated camera pointing the floor based on circular points. The system should extend an existing prototype introducing a robust mechanism for tracking of feature points, and by integrating possibly available information about the robot motion.

Material:

  • existing prototypical implementation of the system

Expected outcome:

  • an odometric sensor for planar odometry with uncalibrated camera

Required skills or skills to be acquired:

  • Good mathematical background
  • Backgroundd in computer vision
  • C++ programming under Linux
Tutor: [[VincenzoCaglioti | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2012
Students: 1 - 2
CFU: 10 - 20
Research Area: Computer Vision and Image Analysis
Research Topic: None

Wiki Page: Odometric system for robots based on laser mice
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Title: Odometric system for robots based on laser mice
Description: We developed an odometric system for robots by combining the reading of several laser mice. The system consists of a master PIC-based board and several slave boards where the sensors employed in optical mice are located. The readings are collected on the PIC and sent on the serial port to a PC which elaborates and combines the x and y readings in order to obtain a x,y,theta estimation of the movement of the robot.

The aim of the project is first to improve the current design of the PIC-based board, and realize a new working prototype, and then to implement and evaluate different algorithms able to estimate more precisely the x,y and theta odometric data from the mice readings. Experience with PIC-based systems and some experience with electronics circuits is a plus. Students are supposed to redesign the electronic board, improve the firmware of the PIC, and work on the algorithm that estimates the robot position on the PC. It would be also interesting to evaluate the possibility to embed the optimization and estimation algorithms in the firmware of the PIC in order to produce a stand-alone device.

Ask the tutors of the project for extra material, such as data-sheets and other documentation.

Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 October 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Robotics
Research Topic: Robot development

Wiki Page: P300 BCI
Title: P300 BCI for ALS patient
Description: Recovery, integration and adaptation of P300 BCI (hardware and software) stubs to generate a working interface for a speller. The aim is to develop a working prototype for an ALS affected patient.
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2011
Students: 1 - 3
CFU: 2 - 20
Research Area: BioSignal Analysis
Research Topic: EEG analysis, classification algorithms

Wiki Page: Poit cloud SLAM with Microsoft Kinect
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Title: Point cloud SLAM with Microsoft Kinect
Description: Simultaneous Localization and Mapping (SLAM) is one of the basic functionalities required from an autonomous robot. In the past we have developed a framework for building SLAM algorithm based on the use of the Extended Kalman Filter and vision sensors. A recently available vision sensor which has tremendous potential for autonomous robots is the Microsoft Kinect RGB-D sensor. The thesis aims at the integration of the Kinect sensor in the framework developed for the development of a point cloud base system for SLAM.

Material:

  • Kinect sensor and libraries
  • A framework for multisensor SLAM
  • PCL2.0 library for dealing with point clouds

Expected outcome:

  • Algorithm able to build 3D point cloud representation of the observed scene
  • Point clouds processing could be used to improve the accuracy of the filter as well

Required skills or skills to be acquired:

  • Basic background in computer vision
  • Basic background in Kalman filtering
  • C++ programming under Linux
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 10 - 20
Research Area: Computer Vision and Image Analysis
Research Topic: None

Wiki Page: R2P IMU firmware development
R2P IMU.png
Title: Embedded Inertial Measurement Unit for Unmanned Aerial Vehihcles
Description: We have developed the electronics of an Inertial Measurement Unit based on an ARM microcontroller to be integrated on an autonomous embedded aerial platform. The IMU has already some attitude heading reference system (AHRS) code implemented, but we are interested in:
  • implementing embedded algorithms for the estimation of the IMU attitude to be compared with the actual one (e.g., Kalman filter, DCM, Madgwick, etc.)
  • developing a, easy to use, procedure for the calibration of IMU parameters
  • making a comparison with commercial units using a robot arm as testbed
  • validate the accuracy of the IMU on a flying platform
  • integrate the measurements from a GPS to reduce drift and provide accurate positiong (this will make it definitely a MS thesis)

Material

  • electronic board and eclipse based C development toolkit for ARM processors
  • papers describing the algorithms we are interested in implementing

Expected outcome:

  • few different AHRS algorithms with comparative results
  • user-friendly procedure to calibrate the IMU
  • a sistem which integrated IMU and GPS to provide accurate positioning

Required skills or skills to be acquired:

  • C programming on ARM microcontroller
  • background on kalman filtering and attitude estimation
Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 January 2015
Students: 1 - 2
CFU: 2 - 20
Research Area: Robotics
Research Topic: Robot development

Wiki Page: ROS navigation local planner
Title: ROS_navigation_local_planner
Description: The project will be focused on the implementation of a planner and tracking algorithms for Ackermann vehicles
Tutor: {{EmailViz|1=[[:User:MatteoMatteucci}}, MatteoMatteucci (), [[]] | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: October 2017
Students: 1 - 3
CFU: 2 - 20
Research Area: Robotics
Research Topic: Local Planner

Wiki Page: Robocentric MoonSLAM
RobocentricSLAM.gif
Title: Robocentric implementation in the MoonSLAM framework
Description: Simultaneous Localization and Mapping (SLAM) is one of the basic functionalities required from an autonomous robot. In the past we have developed a framework for building SLAM algorithm based on the use of the Extended Kalman Filter and vision sensors. The actual implementation of the EKF SLAM in the framework developed uses a world-centric approach, but from the literature it is known that a robocentric approach can provide higher performances on small maps. We would like to have both implementation to compare the results in two scenarios: pure visual odometry, conditional independent submapping.

Material

  • A framework for multisensor SLAM using the world centric approach
  • Papers and report about robocentric slam

Expected outcome:

  • a fully functional robocentric version of the MoonSLAM framework

Required skills or skills to be acquired:

  • Basic background in computer vision
  • Background in Kalman filtering
  • C++ programming under Linux
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2012
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: None

Wiki Page: Robot Games
Spykeecontorri.jpg
Title: Robot Games
Description: Projects may include the design of an interactive game on an existing or a new robot, and its evaluation. These projects allow to experiment with real mobile robots and interaction devices. Some games may be designed for disabled children. The project can be considered a MS thesis if it can produce a new game and, possibly, a new robot, and includes adapting the behavior of the robot to the player.
Tutor: [[AndreaBonarini | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start:
Students: 1 - 2
CFU: 2 - 20
Research Area: Robotics
Research Topic: Robogames

Wiki Page: Scan Matching Odometry and Multisensor SLAM
ScanMatching.jpg
Title: Scan Matching Odometry and Multisensor SLAM
Description: Starting from some C/C++ code for laser scan alignment and the covariance information associated to the matching, we are interested in the development of a library for the matching and fusion of laser scans under the ROS (www.ros.org) environment. From this we are interested in the development of an odometric system based on laser scan matching and in a Simultaneous Localization and Mapping system integrating scan matching with visual SLAM. The result is a complete navigation system that fuses laser and visual information to build consisten maps in an EKF-based environment.

Material:

  • a MS thesis which describes the scan matching algorithms
  • a BS thesis which implements a prototype of the system

Expected outcome:

  • a complete system that build maps integrating laser scan and visual informtion

Required skills or skills to be acquired:

  • Background on Kalman filtering
  • C++ programming under Linux
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2012
Students: 1 - 2
CFU: 10 - 20
Research Area: Robotics
Research Topic: None

Wiki Page: Self calibration of multiple odometric sensors
HandEye.jpg
Title: Self calibration of multiple odometric sensors mounted on the same platform
Description: An odometric sensor measures the path followed by a robot in an incremental way (e.g., wheel mounted encoders, visual odometry, scan matching based odometry, etc.) . Having several odometry sensors mounted on the same platform can significantly improve the accuracy and robustness of the overall system but requires proper calibration of relative positioning and possible biases. We are interested in the development of techniques for the self calibration of a multi sensor based odometry sensor. These techniques could be inspired by classical non-linear optimization techniques used in the hand and eye problem but they could use techniques from Simultaneous Localization and Mapping. According to the setup, some information on the real position of the system may exists (i.e., external tracking system or GPS); the approach should be able to use this information as well.

Material:

  • datasets with real data
  • a few odometric system implementations
  • C++ libraries for non linear optimization

Expected outcome:

  • software for the self calibration of a set of odometry systems mounted on the same robot

Required skills or skills to be acquired:

  • C++ programming under Linux
Tutor: [[MatteoMatteucci | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start: 1 April 2012
Students: 1 - 2
CFU: 20 - 20
Research Area: Robotics
Research Topic: None

warning.png"Agents, Multiagent Systems, Agencies" is not in the list of possible values (Affective Computing, Agents - Multiagent Systems - Agencies, BioSignal Analysis, Computational Intelligence and Games, Computer Vision and Image Analysis, E-Science, Machine Learning, Philosophy of Artificial Intelligence, Robotics, Social Software and Semantic Web) for this property.
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