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Online Collaborative Time Management System using Artificial Intelligence - Planning Project Advisor: Dr. Chris Pollett Committee Members: 1) Dr. Robert Chun 2) Dr. Teng Moh Anand Sivaramakrishnan Agenda • • • • • • Why Planner? Building a Planner Project Algorithm Usability Test Conclusion Why Planner? • Existing Online Planners save paper (because users do not buy notepads) and also have an elegant UI. Eg, Grademate, Playware, Google • Sequence of actions displayed as inputted (organized). They lack intelligence (logic) • My project is about creating a planner that offers advise as to what sequence to be followed, like a consultant • The planner should have a brain (to think) of its’ own instead of just recording user inputs and displaying them as reminders Planning Definition: Generation of a sequence of actions to achieve a goal is called planning. This is exactly what the backend of the product does when it is fed with user inputs such as actions and goals. Building a Planner • Situation Space Planner – World State • There are two types of planners: Progression Planner: It searches forward from the initial situation to the goal situation. Regression Planner: It searches backward from the goal situation to the initial situation. State Space Planning Project • Name – MasterPlanner • Purpose – Planning using Artificial Intelligence, Regression Planning • The Website is collaborative in nature. It’s basic purpose is to achieve collaborative planning among groups • The UI has been tried to be made as user friendly and easy as possible • A complex back end used to make the front end as simple as possible Algorithm – Regression Planning • Traversal from Goal State to Initial State (Backward Heuristic Search). • This project makes use of Regression Planning because: (a) Relevant Actions (b) Lower Branching Factor Algorithm - Unification • Actions are chosen based on Unification between Preconditions of actions(goal in the first iteration) we are going to satisfy and the actions whose effect unifies with the above precondition. • The action is chosen only if all the effects of an action unifies with all the predicates in the World State. Unification • Unification – Pass fruit(apple,pie) vs fruit(apple, X) • Unification – Fail 1) fruit(apple,pie) vs 2) team(andy, Captain) vs team(Captain, jim) fruit(pie, apple) Algorithm - Objects Classes and Objects - Predicates • General AI - Classes / Common Nouns home( b, x) AND car (a) • Website – Objects / Proper Nouns john_home( Clean, Lights) AND andy_car( Engine, Fuel) Algorithm - Backtracking • Ensures no conflicts – Logical sequence of actions based on Unification • If limited actions then will implement backtracking • If it is still not able to satisfy a precondition, then it will report failure, prompting the group members to add more actions World State (Table-DB) Item Property Value Action Home Lights on 78 Home Cleanliness in-complete 43 Home Food ready 22 Collaboration • Actions and Goals are group specific • Groups can add any objects as items, again items are group specific • Flexible Group Involvement • Accountability • No Sub Groups • Scope – Non Corporate Networks Test Cases - Demo Usability Test Feedback • Cryptic terms like Preconditions and Effects are confusing and uneasy to understand • Un-join Groups is again difficult to understand, a term like ‘Leave Groups’ is easier to understand • Empty Database for new Groups, therefore for starters (groups new members) this could be very frustrating • Lack of subsections within a Group such as finance-team, accounts-team • No dates or deadlines Response to User Feedback • The caption ‘Un-Join Groups’ has been changed to ‘Leave Groups’ • Also terms like ‘Preconditions’ and ‘Effects’ will be changed to ‘Before’ and ‘After’ • We could pre add a few items and corresponding properties by default to every new group in the system Conclusion • User – Freedom and Flexibility • Learnt how a system in planning is built. Was very keen to do it • Implementing the algorithm and designing the database for the system were the most challenging tasks • Keen on getting the site on production References • [1995] Artificial Intelligence: A Modern Approach. Peter Norvig, Stuart Russell. Prentice Hall Series. 1995. • [1999] Recent Advances in AI Planning. Sussanne Biundo, Maria Fox. ECP, Springer. 1999. • [1997]Craig Knoblock, Qiang Yang Relating the Performance of Partial Order Planning Algorithms to Domain Features . SIGART Bulletin, Vol. 6, No. 1, 8-15 Thank You Q&A