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Ganesh Chile's Projects

bayes-net-robotics icon bayes-net-robotics

State Estimation of an Agent using Observations | AI for Cognitive Robot Intelligence(COL864) @ IIT Delhi

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Learning SOAR architecture and creating agents as a part of the course as well as personal projects

cognitiveannotationtool icon cognitiveannotationtool

Automatic Annotation tool for labelling images in bulk with their corresponding bounding box annotations.

fish-network icon fish-network

Background There may be many forms of selective pressure acting on the evolution of cognitive ability. One hypothesis is that complex social environments lead to the evolution of increased social cognition. However, a consistent framework for quantifying the complexity of a social environment and the precise factors that influence that complexity has yet to be created. Aims The aim of this project is to assess how the parameters of different networks affect selective pressure on the evolution of cognitive ability in simulations. Strategy This agent-based model is centered around an energy trade-off and the costs and benefits of repeated games between agents. Agents gain energy by consuming resources and expend energy for maintaining their body size and brain; excess energy is stored and can be used when interacting with other fish. To consume a resource, fish enter a repeated Hawk-Dove game with another fish. The interaction ends when at least one Fish plays Dove or when one fish dies. Fish die if their energy reaches zero and during natural mortality that is weighted by their stored energy. Each fish is defined by its body size investment, cognitive investment, and behavioral strategy. At any given time step, a fish also has an energy level, a specific memory, and a position in a network. When fish die, they are replaced by mutated clone of surviving fish weighted by their stored energy. Over time, we expect that different networks will produce different distributions of body size investment, cognitive investment, and behavioral strategy.

pyclarion icon pyclarion

Experimental Python implementation of the Clarion Cognitive Architecture

python_actr icon python_actr

A Python implementation of the ACT-R cognitive Architecture

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First activity for the class: Cognitive and adaptive agents

soar-python-minimum-working-example icon soar-python-minimum-working-example

A minimum working example for using the SOAR cognitive architecture with python for creating an agent that interacts with the environment using SOAR's input-output links.

tasks-structure-cognition icon tasks-structure-cognition

Under the task cognition aspect, we understand categorizing tasks into routine, semi-cognitive, and cognitive based on task clarity (clear rules) and automation potential. The cognition aspect is closely related to the structure aspect, as we organize the identified parts of speech (the structure aspect) – Resources, Techniques, Capacities, Choices (RTCC) – into the three levels of Decision-Making Logic (DML) with the help of the taxonomy vocabulary . Hereby, we distinguished the following three DML levels: (i) routine DML level tasks are those expressible in rules [97]; (ii) semi-cognitive DML level tasks are those where no exact ruleset exists, and there is a clear need for information acquisition and evaluation; (iii) cognitive DML level tasks are the most complex ones where not only information acquisition and evaluation is required but also complex problem-solving.

testgit icon testgit

Config files for my GitHub profile.

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