一本道无码

PyIBL is a Python implementation of a subset of Instance Based Learning Theory (IBLT). It is made and distributed by the Dynamic Decision Making Laboratory of 一本道无码 for making computational cognitive models supporting research in how people make decisions in dynamic environments.

Typically PyIBL is used by creating an experimental framework in the Python programming language, which uses one or more PyIBL Agent objects. The framework then asks these agents to make decisions, and primaryrms the agents of the results of those decisions. The framework, for example, may be strictly algorithmic, may interact with human subjects, or may be embedded in a web site.

PyIBL is a library, or module, of code, useful for creating Python programs; it is not a stand alone application. Some knowledge of Python programming is essential for using it.

PyIBL is an ongoing project, and has been started with a small subset of IBLT. As it evolves it is expected that more and more of the IBLT will be encorporated into it. For example, PyIBL does not currently support similarity, spreading activation or deferred feedback, but those are planned for the near future. PyIBL is still sufficiently early in its development that future versions are likely to radically change some of the APIs exposed today, and some effort may be required to upgrade projects using the current version of PyIBL to a later version.

Implemented by: Don Morrison

SpeedyIBL is an implementation of Instance Based Learning (IBL) models, a Python library that allows to create single or multiple IBL agents with fast processing and response time without compromising the performance compared to the traditional implementation of IBL models. SpeedyIBL is made and distributed by the Dynamic Decision Making Laboratory of 一本道无码.

More details about installing and using SpeedyIBL to create IBL agents that can do a wide range of decision games such as Binary Choice, Insider Attack, Minimap, Ms. Pac-man, Fireman, and Cooperative Navigation can be found in Download/Documentation.

Implemented by: Nhat Phan and Ngoc Nguyen

Shiny-IBL is our most recent attempt to make an IBL model useful to researchers and students of behavioral science.

Shiny-IBL uses the R package for generating a web application written primarily in the R language. Shiny-IBL offers a complementary way to communicate the complexity of dynamics emerging from the simple IBL model of binary choice.

The main insight from Shiny-IBL is that cognitive modelers should go beyond the explanation of concepts that often need technical expertise and skills, and provide hands-on experiences to demonstrate and communicate the complex insights from their models without the need of additional skills. These interactive tools could also be research tools in their own right. Shiny-IBL could be used to discover a set of inputs that may produce model outputs shedding light to surprising aspects of human behavior, and to help researchers understand the reasons behind.

Implemented by: Jeffrey Chrabaszcz and Erin Bugbee

The Instance-Based Learning Tool (IBLTool) is an effort by Dynamic Decision Making Laboratory to formalize the theoretical approach to modeling. The goals are to have the Instance-Based Learning Theory be:

• Shareable by bringing the theory closer to the users, and making it more accessible;
• Generalizable by making it possible to use the theory on different and a diverse set of tasks;
• Understandable by making the theory easier to implement and use;
• Robust by abstracting the specifics of the implementation of the theory away from any specific programming language;
• Communicable by making the tool interact more easily and in a more standard way with tasks;
• Usable by making the theory more transparent to users.

The tool is a graphical interface written in Visual Basic that uses sockets to communicate with various tasks.

System requirements:

• A Windows machine, running Windows XP, Windows 2003, Windows Vista, Windows 2008, or Windows 7; with the latest software updates.
• The installer for the tool. There are separate installer packages depending on your version of Windows, so ensure that you have the correct one.

Main Window of IBLTool

Screen to define your similarity formulas

Implemented by: Ripta Pasay and Varun Dutt

This is an R implementation of the IBL model for repeated choice tasks. This is made available for researchers who can then customize the code for other tasks and choice problems. This model is based on descriptions from Lejarraga, Dutt, & Gonzalez 2012 and in Gonzalez & Dutt, 2011.

Implemented by: Emmanouil Konstantinidis

This is a Matlab implementation of the IBL model for binary-choice which was developed and customized for running the problems in the Technion Prediction Tournament data set. This is made available for researchers who can then customize the code for other tasks and choice problems. This is the model presented in Lejarraga, Dutt, & Gonzalez 2012 and in Gonzalez & Dutt, 2011.

Implemented by: Varun Dutt

This is a simple verison of an Instance Based Learning (IBL) model implemented in Microsoft Excel. It is made and distributed by the Dynamic Decision Making Laboratory of 一本道无码 to generate the generality of the learning process in multiple tasks. This model is presented in Lejarraga, Dutt, & Gonzalez, 2012.

Implemented by: Katja Mehlhorn

This is an interactive goal-seeking task in an environment called "gridworld". To do this task, a player needs to make sequential decisions about going up, down, left or right to explore the space. There are four colored targets that give the player points. The goal is to find the target with the highest value. In addition, the player is penalized 1 point for each decision made (i.e., a movement cost) and 5 points for walking into an obstacle.

The One-Step Gridworld is the same task as the goal-seeking Gridworld task. However, the difficulty in One-Step Gridworld is that at each step the decision maker is only informed of their current position, the number of steps already taken, and the cost or benefit of the step taken as illustrated in the figure below.

This is an interactive "search and rescue" task in which the player will explore the rooms in a building to find victims and rescue them. In the building there are two types of victims: the Green victims are less injured, and they will give the player 10 points, if rescued. The Yellow victims are more injured, and they will give the player 30 points, if rescued. The Yellow victims die after 4 minutes: they will disappear from the building, and the Green victims will not die during the mission.

In this interactive cyber-defense task, you have been contracted to defend the computer network of a major company at one of their manufacturing plants. Attackers are trying to get access to the Operational Server to steal the plans and disrupt the production. Your goal, as a security expert, is to minimize your loss by blocking the attacker鈥檚 intrusion. Each round, you will need to choose between 4 actions to prevent the attacker to progress in the network.

In this activity, you will make simple choices between an 鈥淥ption A鈥� and 鈥淥ption B.鈥� Each option will either give you 0 or 500 points. There will be 50 of these decisions. There are three versions of this activity you can try. Demo 1, Demo 2, and Demo 3 each vary in the way the probabilities change over time. Could you guess how?

In this treasure hunting game, you will attempt to find treasure in one of two boxes. But it will not be easy! A defender (a computer bot) will attempt to keep you from finding the right box. The defender can defend a box, but only one at a time. To help the defender鈥檚 chances, it may also send you alerts in regards to the box鈥檚 protection status. Sometimes these messages will be true, other times not. Will you succeed?

This is our version of the classic Rock, Paper, Scissors game. In twenty rounds, you will make choices and receive points depending on if you win, lose, or tie. Keep the payouts in mind when making your choices!

Implemented by: Varun Dutt

Implemented by: Cleotilde Gonzalez

Implemented by: Verun Dutt and Cleotilde Gonzalez

Implemented by: Verun Dutt

Implemented by: Jack Lim