IIIT Hyderabad Publications |
|||||||||
|
Idle time driven decision making by Always-On AgentsAuthor: Sravya Sri Garapati Date: 2022-04-16 Report no: IIIT/TH/2022/44 Advisor:Kamalakar Karlapalem AbstractAlways-on agents are those agents which are like daemon programs that are always on and can do tasks as and when they arrive. These agents are idle when there is no task assigned to them. Further, those agents that work on a task also wait for some event or task completion, and hence, are also idle for short durations while executing the tasks. The question arises what should the agent be doing when it is idle. In this thesis, we conduct an empirical analysis to show improved decision-making capabilities by agents when they exploit their idle time. We analyze scenarios where (i) the agents analyze their past tasks regarding decisions taken and their impact and where (ii) the agents cooperate with other agents to improve their decision-making. Performance improvement can be measured by considering an increase in success rate, avoiding strategies that may not work, quicker decision making, etc. While executing a task, our always-on agent stores pertinent details of the task done in a database, such as decisions taken, paths of execution of the task, goodness of them, etc. The agent uses different strategies to use this stored knowledge. We present and evaluate three strategies that always-on agents can use (i) Frequent Decision Strategy (FDS) - the agent stores the prior executions and their frequency of success and failure, repeats the most frequent successful decision taken during prior executions of the task, (ii) Analyzed Decision Strategy (ADS) - the agent analyses prior executions that were successful or not, stores in the database the goodness of various alternatives and chooses the best alternative and (iii) Online Analysis Decision Strategy (OADS) - the always-on agent while executing its task, during its idle time analyses the possible future situations and prepares the list of best possible decisions that can be taken in future. The FDS and ADS are used when the agent is not doing any task and is off-line. In contrast, OADS generates new mock tasks to consider possible alternative task execution situations to expand its decision-making scenarios while having a task at hand. We conduct our empirical study on always-on agents playing Connect-4 games to check the viability and improved decision-making. We also present idle time analysis in always-on cooperative agents. We propose a Multi-agent Framework that always-on cooperative agents can use in their idle time to improve decision-making. We formulate scenarios in which the agent has a rationale to cooperate with other agents (i) Exception Handling - the agent cooperates to handle situations in which it does not know what to do, (ii) Confidence Boosting - the agent cooperates to boost confidence in actions it wants to take, (iii) To obtain Different Experiences - the agent cooperates to obtain different experiences. We conduct an empirical study on always-on cooperative agents using the Multi-agent Framework and show improved decision-making. Full thesis: pdf Centre for Data Engineering |
||||||||
Copyright © 2009 - IIIT Hyderabad. All Rights Reserved. |