IMARA Team definition Toward a knowledge model Diogo
- Slides: 58
IMARA Team definition Toward a knowledge model Diogo DE SOUZA DUTRA 21/01/2010
Goal IMARA Team definition Why defining a team? Knowledge Capitalization Group/ personnel organization Data base organization Defining Goals Establish strategies to research or to partnerships Diogo DE SOUZA DUTRA 21/01/2010
Goal IMARA Team definition As a research team, IMARA can be defined by its knowledge. * A team knowledge can be described by: What the team know - it actual state (static) that can be described by the sum of individuals knowledge. What the team want to know - its process to acquiring new ones (dynamic). Knowledge model • Model of static knowledge • Model of knowledge dynamic To define IMARA Team it will be necessary create a Knowledge Model that takes into account “static” (actual knowledge) and “dynamic” (process of acquiring knowledge) characteristics. *savoir-faire is one kind of knowledge Diogo DE SOUZA DUTRA 21/01/2010
Goal - IMARA Team definition Create a Knowledge Model There is several approaches to model knowledge: Epistemology (philosophy) Cognition (psychology) Neurosciences (Biology) Information System (Informatics) All these approaches explains from different points of view. C-K Theory is a recent approach to model knowledge that seems to explain and to link all these approaches. * * this theory was born in 1999 at Ecole de Mines de Paris. Diogo DE SOUZA DUTRA 21/01/2010
Goal - IMARA Team definition – Knowledge Model C-K Theory Was create to explain the design process. C-K theory explain the design process using: Concept Space Knowledge Space Processes inside Concept Space Processes inside Knowledge Space Process between two Spaces • The main goal is to define IMARA team using a knowledge model that helps to model actual knowledge and the process of creating new knowledge. • Unlike others Knowledge model theory, CK explain very well Knowledge acquiring on the process of designing something. Diogo DE SOUZA DUTRA 21/01/2010
Goal - IMARA Team definition – Knowledge Model Using C-K and others modeling theories it appears possible to do a complete Knowledge Model to define IMARA Team. Knowledge model Modeling theory C-K Theory Diogo DE SOUZA DUTRA 21/01/2010
Building a model IMARA Team definition Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team definition - Building a model Knowledge Space What we know/do Concept Space What we want to know/do Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team definition - Building a model On Knowledge Space there all actual knowledge that IMARA team has. On Concept Space there all concepts that IMARA want to reach. It represents the knowledge to be reached. The process represented by the arrows represents: Knowledge acquiring (from concept to knowledge) New strategies (from knowledge to concept) The goal is to represent actual knowledge and the process of acquiring new ones. Therefore, the model that we search is represented by: Knowledge Space What we know/do Diogo DE SOUZA DUTRA Concept Space What we want to know/do 21/01/2010
Concepts - What we want to know/do IMARA Team definition Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team definition - Building a model Knowledge Space Concept Space What we want to know/do Diogo DE SOUZA DUTRA 21/01/2010
Concepts - What do we want to know/do? • To define what IMARA want to do is was necessary to see the main team goal. IMARA Site goal definition: Road transportation improvement on: Safety, Comfort, Efficiency and Ease of use of road transport ITS The goal can be resumed in one concept: Intelligent Transportation System. What is ITS? How can we define it? ITS Modeling Diogo DE SOUZA DUTRA 21/01/2010
Intelligent Transportation System (ITS) How can we define ITS? What is “Intelligent” ? What is a “Transportation system” ? To define ITS we have to define each concept that form the idea of ITS: Intelligent and Transport System. Two modeling approaches To define “Transport System” to reach an “Intelligent Transport System” modeling. To define “Intelligent” to reach an “Intelligent Transport System” modeling. Diogo DE SOUZA DUTRA 21/01/2010
Intelligent Transportation System (ITS) Two modeling approaches Bottom-up Transport System Intelligent Transport System ex: I drive-> I want to go to Marseille -> I need a help Top-down Intelligent • On this case an use case of transport system is used to find how this use can be “Intelligent”. Intelligent Transport System ex: I know stabilize a dynamic system -> I want to park a car • On this case an Intelligent way to do things is used to make a task Intelligent. Diogo DE SOUZA DUTRA 21/01/2010
Intelligent Transportation System (ITS) Observations To model using these 2 approaches a huge background is needed. To reach a good model some teams reunions should be made to reach the best possible global model. On this work it wasn’t possible to join all group. A good work was made with control team on Top-down model. However, Bottom-up model was made with a few knowledge and this example of model can appear false. Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Bottom-up IMARA Team definition - Concepts Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Bottom-up How can we define a Transportation System? Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Bottom-up How can we define an Intelligent Transportation System? What is intelligent will depend on the point of view: Use-case examples Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Bottom-up Intelligent Driving Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Bottom-up Intelligent Driving Diogo DE SOUZA DUTRA Intelligent Parking 21/01/2010
ITS Modeling – Bottom-up Intelligent Movement Intelligent Driving Diogo DE SOUZA DUTRA Intelligent Parking 21/01/2010
ITS Modeling – Bottom-up Intelligent Parking Intelligent Driving Intelligent Parking Intelligent Mobility Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Bottom-up An concept map example Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Top-Down IMARA Team definition - Concepts Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Top-Down How can we define Intelligent? Intelligent Living organisms Senses Think to an appropriate action to do Smart Action Diogo DE SOUZA DUTRA 21/01/2010
ITS Modeling – Top-Down How can we define an Intelligent Transportation System? (Intelligent artificial organisms) Robotics model Sensors Perception Comm receiver Planification Actuators Comm transmitter HMI Control HMI Knowledge Base Supervision Diogo DE SOUZA DUTRA 21/01/2010
Knowledge IMARA Team definition Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team definition - Building a model Knowledge Space Concept Space Perception Sensors Actuators Planification Comm receiver Comm transmitter HMI Control HMI Memory Supervision What we know/do Diogo DE SOUZA DUTRA 21/01/2010
What do we know/do? The knowledge of a group can be model as the sum of each individual knowledge. Individual Knowledge: Background, Expertise, Savoir Vehicle Stability faire; ∆K Control Theory K K+∆K t Diogo DE SOUZA DUTRA 21/01/2010
What do we know/do? The Knowledge produced by older workers will be a base for new knowledge production. Vehicle Stability ∆K= Ki + new K Control Theory K+Ki K t Diogo DE SOUZA DUTRA 21/01/2010
What do we know/do? It’s important for the group a “materialization” of this knowledge production. This materialization can be divided in: Platform: All material and documents to use the material (physic and virtual) Academic: All academic productions (articles, standards…) Hardware Platform Software Publications Knowledge Academic Standards Reports CV Personnel Expertise Diogo DE SOUZA DUTRA 21/01/2010
Why defining a team? Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team definition - Building a model Why defining a team? Knowledge Space What we know/do Concept Space What we want to know/do Knowledge Capitalization Defining Goals Diogo DE SOUZA DUTRA 21/01/2010
How can we use this? Knowledge Space Platform: -Material -Virtual Concept Space Perception Sensors Actuators Planification Comm receiver Comm transmitter HMI Control HMI Memory Supervision Academic: - Classification by classical domains - Classification by concept application Personnel Diogo DE SOUZA DUTRA 21/01/2010
Defining Goals Using the concept models it’s possible to find what IMARA does and what it doesn’t! It’s possible to classify projects and partners! It’s possible to see what need to be explore! Diogo DE SOUZA DUTRA 21/01/2010
Example: Defining Goals Example of classification: IMARA - LARA Diogo DE SOUZA DUTRA 21/01/2010
Example: Defining Goals Example of classification: IMARA - LARA Vehicle 1 Structure Perception Sensors Actuators Planification Control HMI Memory Supervision Traffic Modeling Perception Sensors Actuators Planification Control HMI Memory Supervision Vehicle 11 Diogo DE SOUZA DUTRA 21/01/2010
Knowledge Capitalization Using the knowledge model, it’s possible to define a first idea for a data base. Using some concepts as classification it will be easy to materialize and capitalize new knowledge. Diogo DE SOUZA DUTRA 21/01/2010
How can we use this? Knowledge Space Platform: -Material -Virtual Materialization Se ns or Com s m Percept ion Planific ation recei ver H MI Concept Space Instantiation & Classification Control Act uat Com ors m trans mitte r HMI Perception Sensors Actuators Planification Comm receiver Comm transmitter HMI Memor y Control Supervi sion HMI Memory Supervision Academic: - Classification by classical domains - Classification by concept application Personnel Diogo DE SOUZA DUTRA 21/01/2010
Space interaction Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team definition - Building a model Knowledge Space What we know/do Concept Space What we want to know/do Knowledge Capitalization Defining Goals Diogo DE SOUZA DUTRA 21/01/2010
Space interaction Knowledge Space Concept Space Project Finished Project New Projects Diogo DE SOUZA DUTRA 21/01/2010
Space interaction The inputs for new knowledge at INRIA are by Projects. The projects arrives as a concept to be materialized. It’s impossible to do a generic solution for all situations of the concept. The engineering approach to solve problem separates the problem in parts and the solution is for each part. To separate the problem in parts there is some hypothesis and limitations to be done. Example: I want an intelligent driving. To solve this concept I have to restrain the problem to “intelligent driving” on highways. On highways I will restrain to indentify the lines on the ground. Now I can build an Intelligent driving. Each context put as restriction will form a scenario. The sums of scenario on the end can form the whole concept. Diogo DE SOUZA DUTRA 21/01/2010
Perspective for this model IMARA Team definition Diogo DE SOUZA DUTRA 21/01/2010
Perspective for IMARA Team definition Concept Models: Bottom-up: From this work the idea and the methodology to build the model can be used to reconstruct and to build a more complete map. Using several levels it will be several levels of classifications to situate projects and the whole group. As the map is right now it can be lead to a lot of discussion on concepts mining. Top-down: Is a more elaborated model. It can already be used to explore each block to find internal classification until the most basic component and the real one. Knowledge model: Can be used as a base to start a data base. To create a data base it’s useful to think what are the means to search. To include an additional information an block “End Projects” could be added. On this project, an scenario entity describes the limitations used to the concept that was search. This kind of reference between projects, people and data can be already made on Wiki and SVN. Diogo DE SOUZA DUTRA 21/01/2010
Perspective for IMARA Team definition Knowledge Capitalization Defining Goals Group/ personnel organization: With a good model it will be possible to separate teams to work together. For example: Communication group. That could be interesting to have a control, a perception, a sensor and etc. Data base organization: Using knowledge model with concept model classification it will be possible to do a very good search when it is necessary to find an information. If knowledge model is used as storage classes and the concepts in concepts model as tags, the data base will be very well organized and easy to use. Establish strategies to research or to partnerships: With a good concept model it will be easy to classify projects and general/specific group knowledge. With that it will be and look for new partnerships. easy to find deficiencies. Example: We saw that we don’t do to much work on Supervision. IMARA Team common language With a very good concept model it will be easy to explain with a map or a schema what the projects do and what we do on the project. Diogo DE SOUZA DUTRA 21/01/2010
Proposition - Example Platform: Hardware Software Academic Personal Sensors Perception Comm receiver Actuators Planification HMI Control HMI Memory Classification for each topic: HMI Communication Perception Planification Control Supervision Memory/Models Sensor Actuators Comm transmitter Supervision Wiki, SVN…. Diogo DE SOUZA DUTRA 21/01/2010
Proposition - Example Platform: Hardware Software A new person arrives on planification group Academic Personal Tags du Wiki Classification for each topic: HMI Communication Perception Planification Control Supervision Memory/Models Sensor Actuators Easily access to personal, academic production and platforms produced by the group Diogo DE SOUZA DUTRA 21/01/2010
IMARA Team common language and Examples Diogo DE SOUZA DUTRA 21/01/2010
HAVEit Vehicle 1 Infrastructure Perception Sensors Actuators Planification Control HMI Memory Supervision Vehicle 11 Diogo DE SOUZA DUTRA 21/01/2010
HAVEit Vehicle 1 Sensor s Perception Infrastructure Actuator s Planification Control HMI Memory HMI Supervision Diogo DE SOUZA DUTRA 21/01/2010
HAVEit - Actuators Vehicle 1 Perception: Sensors: -Vehicle data -RTK-GPS -Laser Scanner -Camera -Radar Comm receiver: -Speed limit HMI: - Driver Inputs -Driver State -Lane Detection -Obstacle Detection -Vehicle State Planification: -Manouvre planning -Trajectory planning Control: -Lateral Controller -Longitudinal Controller Actuators: -Accelerator -Brake -Haptic Feedback steering - Servo-steering HMI: -Inform -Warning -Advice Knowledge Base: - Detailed Word maps Supervision: -MSU Diogo DE SOUZA DUTRA 21/01/2010
Example Project classification Traffic Light Prediction – Intersafe 2 Intersafe-2 Traffic Light Prediction Diogo DE SOUZA DUTRA 21/01/2010
Example Project classification Traffic Light Prediction – Intersafe 2 Intersafe-2 Traffic Light Perception Planification Sensors Comm transmitter Control Memory Supervision Traffic Light Prediction Diogo DE SOUZA DUTRA 21/01/2010
ABV Vehicle 1 Infrastructure Perception Sensors Actuators Planification Control HMI Memory Supervision Vehicle 11 Diogo DE SOUZA DUTRA 21/01/2010
Geo. Net Vehicle 1 Infrastructure Perception Sensors Actuators Planification Control HMI Memory Supervision Vehicle 11 Diogo DE SOUZA DUTRA 21/01/2010
Picav Vehicle 1 Infrastructure Perception Sensors Actuators Planification Control HMI Memory Supervision Vehicle 11 Diogo DE SOUZA DUTRA 21/01/2010
Merci Diogo DE SOUZA DUTRA diogosdcx@hotmail. com Diogo DE SOUZA DUTRA 21/01/2010
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