Student material
Wednesday, 30 March 2016 17:27

Masters Fair - Meet CAS

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  • Master fair in A-building, Chalmers Univ Johanneberg campus on Tuesday April 12th, 12-15. Meet CAS students. 
  • Lunch meeting, lecture hall FB, Chalmers Univ Johanneberg campus on Thursday April 14th, 12.00.-12.45 Meet CAS teachers.  

 

Meet and interact with MPCAS students and teachers! Learn more about:

Brief information about the master thesis from the MPA: 
  • To find a project: talk to teachers, look for posters, search the web. 
  • The project should be CAS (or physics) related, interpreted in a wide sense. The most important thing is that it has a solid scientific or engineering perspective. 
  • You need an examiner at Chalmers (or GU). (GU students need a GU examiner.) 
  • You need a supervisor at Chalmers/GU or elsewhere, such as in a company. (Examiner and supervisor may be the same person for a local Chalmers project.)
  • Typically the project should be registered at the department where the examiner is active. 
  • Before you start, you need to have the registration form signed by me (MPA), and the examiner. There should be a short project plan which is what I need to see to approve it as a CAS thesis. 
  • And don’t forget to fill in the work card (applies to Chalmers students) once you start on the project. This implies attending two other thesis presentations, as well as being and having an opponent. 
  • More information is found at the studentportal Examensarbete
  • If you plan to do a thesis at another department (i.e. not Physics/Applied Physics) local rules may apply. (This is the case for Computer Science/D&IT)

If you have questions or need help, you very welcome to come and see me. /Mats
 
Monday, 30 November 2015 19:59

Split or Steal Game. Game Theory.

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As part of the game theory student seminar our team played the Split-Steal game in class.

Two players with two actions each, Split or Steal. The treasure was comically 2 gingerbread cookies!

Essentially a form of Hawk-Dove game. Hawk: aggressive (escalates conflict) i.e. Steals , Dove: non-aggressive (backs down from escalation) so naturally Splits.

Different strategies / roles

  • Hawk: Aggressive, never retreats.
  • Dove: Defensive, retreats if opponent is aggressive.
  • Bully: Aggressive against defensive opponents. Retreats if opponent is too aggressive.
  • Retaliator: Defensive, but retaliates aggressively if opponent is aggressive.
  • Prober-retaliator: Defensive, but sometimes makes aggressive probes and only reverts to defensive if the response is aggressive. Retaliates aggressively if opponent is aggressive.

CLICK ON THE LINKS BELOW TO WATCH THE VIDEOS OF THE GAME:

 

Final Part of the game:

More parts of the seminar

Part 02

This game can also be seen as a Bayesian game. Information about characteristics or types of the other players (i.e. payoffs) is incomplete in such games. Nature assigns a random variable to each player which could take values of types for each player and associating probabilities (or a probability density function with those types). At least one player is unsure of the type and the payoffs of another player. Players have initial beliefs about the type of each player and can update their beliefs according to Bayes' Rule as play takes place in the game. The belief a player holds about another player's type might change on the basis of the actions they have played. 

In a Bayesian game setting there are three meaningful notions of expected utility: ex post, ex interim and ex ante. 

  • Ex post: Here EU is computed based on all agents’ actual types (rarely feasible as even the game being played maybe unknown at times)
  • Ex interim: Considers the setting in which an agent knows his own type but not the types of the other agents (more practical)
  • Ex ante: In this case the agent does not know anybody’s type including her own

Analysis of the Game:

 

Conclusion of the analysis.

 

 

 

 

My group's project in Humanoid Robotics course (14/15). We used Chabot robot to be controlled over a network (based on a TCP client-server connection), using a Kinect. Therefore, this system could be use to control the robot on-line from another different place. As can be seen in the video, the robot is able to follow all the arm-movements and also recognizes our hands in order to open or close his clamps.

 

 

In the course Humanoid Robotics, my group used genetic algorithms to let a Bioloid robot evolve a walking behaviour. The training phase (evolution) took place in the simulation environment V-REP. The robot is controlled by a CPG (central pattern generator) which was optimized using a genetic algorithm.

Saturday, 21 November 2015 21:12

Revisiting Nash Equilibrium in Prisoner's Dilemma.

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An often confusing aspect of reading the payoff matrix in a game theory setting (at-least for new comers or those delving into the subject after a hiatus) is the confusion between the row players and column players. A more intuitive method maybe to keep track of the process of propensity of movement of player's states (as per moving in the direction of higher utility) using some sort of color coding to see the direction in which they move. The following figure tries to capture the same - and observe the point where the arrows meet is the location of Nash Equilibrium. I know it may seem too formal an approach to bring in a design aspect (colors and arrows), but then game theory itself is a formalism of something most people would say is common sense, intuition so a bit more formalism wont harm - let me know if this becomes more intuitive:

 

Generic background: As is evident a rational agent has clear preferences (i.e. states that he likes) and always chooses to perform the action with the optimal expected outcome for itself from among all feasible actions. A utility function (in the form of the above payoff matrix) is used to map out real world choices to quantitative numbers. These numbers can be seen to be levels of happiness of the agent in those corresponding states. 

Intuitively, a Nash equilibrium is a stable strategy profile: no agent would want to change his strategy if he knew what strategies the other agents were following. Nash equilibria can be strict and weak, depending on whether or not every agent’s strategy constitutes a unique best response to the other agents’ strategies.

 

 

 

Wednesday, 04 November 2015 17:10

CAS student seminars from the 2014-2015 class

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 Student seminars from the 2014-2015 class - Complex Systems Seminars

 

Dynamical Systems

 


Shamit Bagchi 

"Consciousness as Integrated Information"


Ivo Batkovic 

"Unsupervised Evolutionary Art"


Milica Bijelovic 

"Chaos - Making a new science"


Jens Carlsson 

"Micro-simulation of disease spread"


Siamak Esmi Erkani 

""


Oskar Fridell 

"Matching Social and Ecological Systems in Complex Ocean Fisheries"


Johan Frisch 

"Anticipating Critical Transitions"


Pontus Granström 

"The Fractal Geometry of Nature"


Amrit Krishnan 

"Rethinking Economics Using Complexity Theory"


Marcus Hägerstrand 

"Predicting the Stock Market Using Twitter"


Mats Uddgård 

"Watson - WatsonPaths"


Selvin Cephus Jayakumar 

"Self-Programming Matter and Artificial Life"


Hjalmar Karlsson 

"Cellular Automata Approaches to Biological Modeling"


Jared Karr 

"Turing Tested"


Mattias Kjelltoft 

"Fractal Terrain"


Fredrik Hoxell

"On the Role of Self-Organization in the Development of Individual and Collective Behaviour - How to Implement Advanced Behaviour in Robots"


Paul Lange

"Climate and Weather Simulation - Using Ensemble Methods to Predict Chaos"


Jonathan Larsson

"Models for simulating pedestrian behaviour and escape panic"


Olof Gustavsson

""


Laura Masaracchia

"Human Dynamics, From Small Group Interactions to Anthropology"


Björn Persson Mattson

"Honeybee Democracy - Consensus mechanisms in social insect societies"


Björnborg Nguyen

"Modeling vehicular traffic as a complex system


Elin Romare

"Immunizing well-connected networks - why random immunization doesn't work


Vitalii Iarko

"How I could be a millionaire - or - a bit about BitCoin"


Marcus Schmidt Birgersson

"Prediction and classification of cancer using artificial neural networks"


Niclas Ståhl

"Protein folding"


Alireza Tashivir

"Artificial Immune System inspired by Human Immune Systems"


Sebastian Hörl

"Modeling the dynamics of belief systems"


Nils Wireklint

"Fooling Neural Networks and adversarial examples"


Helga Kristín Ólafsdóttir

"Rumor Spreading Model with Trust Mechanism in Complex Social Networks"


Peter Svensson

"Modeling Chemical Exposures"


Toby St. Clere Smithe

"Methods for a complex system: The neuroscience of visual object recognition"


Tomas Jacobsson

""


Kirill Blazhko

""


Kalle Hansson

""


Gustav Olsson

""

 

Wednesday, 04 November 2015 16:21

Masters Thesis 2015-2016

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Masters Thesis 2015-2016

Featuring the details of thesis projects being conducted by CAS students currently or next spring!

 

ONGOING THESIS PROJECTS

Using situation based predictive risk estimation for subject and surrounding traffic to ensure safe maneuvering of long combination vehicles (LCVs) in highway driving scenarios.

 "When driving long-combination vehicles (trucks so long and heavy that they need a special permit to be driven on roads), it is important that the driver handles the vehicle as safely as possible. This master thesis will explore methods for estimating the risks for the subject vehicle and surrounding traffic which allow safe maneuvering. This will be used both for alerting the driver of dangerous situations and autonomous driving. The work includes adaptive systems and risk analysis, and will be performed at Volvo Group Trucks Technology."

Bjorn Persson Mattsson

 

Multi-level evaluation of autonomous vehicles using agent-based transport simulation for the case of Singapore

Based on an existing large-scale agent-based simulation of Singapore (MATSIM) the impacts of autonomously driving vehicles are examined. The focus of investigation lies on the emerging choice behaviour of customers in the multi-modal public transport scenario. Different pricing scenarios and service schemes are simulated to compare the acceptance and usability of the new technology.

January until June 2016 @ ETH Singapore Center Future Cities Laboratory (FCL) - www.futurecities.ethz.ch

Presentation: mid-July 2016

Sebastian Hörl

 

Real-time optimization of traffic situation maneuvers for long combination vehicles (LCVs)

An approach to improve traffic safety and to further increase transport efficiency of LCVs is to utilize automated driving functionalities including propulsion, braking and steering. This master thesis will propose an optimization scheme which allows real-time actuation generation for highway maneuvers of LCVs. The work includes adaptive systems and programming and optimization, and will be performed at Volvo Group Trucks Technology.

Ivo Batkovic

 


 

Here are some tips from our director on doing thesis project. A summary of information about the master thesis: 

- Start early (i.e. now), looking for a project: talk to teachers, look for posters, search the web. I will also forward occasional project proposals. 

- The project should be CAS (or physics) related, interpreted in a wide sense. The most most important thing is that it has a solid scientific or engineering perspective. 

- You need an examiner at Chalmers. You need a supervisor at Chalmers or elsewhere, such as in a company. (Examiner and supervisor may be the same person for a local Chalmers project.)

- Typically the project should be registered at the department where the examiner is active.

- Before you start, you need to have the registration form signed by the MPA, and the examiner. There should be a short project plan which is what I need to see to approve it as a CAS thesis. 

- Don’t forget to fill in the work card once you start on the project (which implies attending other thesis presentations, etc.) 

- More information is found at the Studentportal Examensarbete

 

If you have questions or need help, you very welcome to contact the Director.

 

Tuesday, 11 August 2015 08:46

Start-up meeting, fall 2015

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New students, welcome to the CAS program!

Come to the start-up meeting on Monday August 31st, 10.15 room FL71 on Chalmers campus Johanneberg.  

 

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