COGENT Cognitive Agent to Amplify Human Perception and Cognition

1. COGENTCognitive Agent to Amplify Human Perception and Cognition Subrata DAS and Dan GRECU 2000 Proceedings of the International Conference on Autonomous Agents ACM, New York, NY, USA pp. 443-450 Presented by Tivil MOK

2. Objectives • To present a cognitive agent, COGENT, to amplify human perception and cognition • Motivation • To provide decision-aiding at multiple levels of information processing (spacecraft operations) in a manner that complements existing human perceptual and cognitive abilities in tasks that humans find difficult • Management of uncertain information • Keeping track of large numbers of details • Detecting conflicts among currently active alternatives • Etc. • Goal of this paper • To illustrate how COGENT integrates several techniques to successfully support alternating reasoning types of the human operator

3. Outline • Background and Approach • COGENT Architecture • Decision-Aiding in COGENT • Evaluation and Implementation • Conclusions and Future Directions

4. Introduction • Decision makers must integrate large quantities of data with  their knowledge and experience to take the best decisions • Today's world is fast-paced, dynamic, constantly changing • incoming data are very diverse and in large amount        • need for systems that help humans process all the information received • need to provide user with relevant and easy to assimilate information • Example : COGENT • Use of COGENT in the context of a ground-based control for communication satellite problem using telemetry data sets

5. Background and Approach • Humans are not good at handling large volume of information, thus decision-making is sometimes difficult • Need for decision-aiding systems                    • to filter irrelevant information                 • to handle information that the human brain cannot (easily) process          • to format the data collected so it is easily understandable  • The system developed is based on Rasmussen three-level model of human information processing                • level 1: skill-based behavior                • level 2: rule-based behavior                • level 3: knowledge-based behavior

6. COGENT Architecture • Decision-aiding module • Maps the hierarchical Rasmussen cognitive processing model • Visualization interface module • Displays the information produced by the decision-aiding module

7. Visualization Interface Module Task and Display User Profile Decision-Aiding Module Operational Data and Domain Knowledge USER Situation Features Alerts Recommendations Response Recommendation Situation Situation Assessment Events Data Preparation Data Stream

8. COGENT Architecture Decision-Aiding Module • Probabilistic rule-based techniques (lowest level) • Situation: Uncertain and/or conflicting incoming data • Are used for event generation and information filtering • Bayesian belief networks • Situation: incomplete descriptions • Implement situation assessment • Argumentation techniques (top level) • Several possible decision alternatives for the operator to select • Are used for response recommendations

9. Summariz. Profile Summarization Filtered data & events Filtering Profile Information Filtering Events Event Profile Reprocessing and Event Generation Decision-Aiding in COGENTData Preparation To situation assessment and visualization modules Payload Data

10. Decision-Aiding in COGENTData PreparationEvent Generation • Event model • Flexible • Intuitive  Necessary means to perform • Data filtering • Data summarization • Data prioritization • Target objects • Continuous-valued parameters • Discrete-valued parameters

11. Decision-Aiding in COGENTData PreparationEvent Generation Continuous Event Continuous Valued Mnemonic Event Types High Red High Yellow High Green Green Low Green Low Yellow Low Red Start Time Value End Time

12. Decision-Aiding in COGENTData PreparationEvent Generation Discrete Event Discrete Valued Mnemonic Event Types Red Green Start Time Value End Time

13. Decision-Aiding in COGENTData PreparationInformation Filtering • Mechanism through which the user can subscribe profiles that are continuously evaluated • Allows COGENT to send filtered events according to the profile • What is a profile ? Set of rules of the form : IF Spacecraft-Mode(X) THEN Filter(Y)

14. Decision-Aiding in COGENTData PreparationInformation Filtering • IF Spacecraft-Mode(normal) THEN Filter({green, yellow}) • IF Spacecraft-Mode(fail-safe) THEN Filter({none})

15. Decision-Aiding in COGENTData PreparationData Summarization • Aim • To save bandwidth without any significant loss of operator perception about the status of the satellite • Two techniques • Parameters correlation • Event clustering

16. Decision-Aiding in COGENTData PreparationData Summarization • Parameters correlation • COGENT gathers relationships between all parameters in correlation matrices • If there is a correlation between two parameters, they shall be analyzed together • Warning limits are statistical (e.g. 1, 2, and 3 ) • Event clustering • Time variant method • Clusters consecutive events within the same zone • Time invariant method • Clusters events within the same zone • Delta method • Clusters events that are considered important or unusual with immediate predecessors and successors

17. Decision-Aiding in COGENTSituation Assessment • Purpose • To capture the data that is part of a causal model • To continuously update the model, and keep the user informed only on the high-level aspects that are of immediate interest • To monitor values that result from the assessment, to identify abnormalities, and to use the model to identify potential causes

18. Bayesian Belief Network Decision-Aiding in COGENTSituation Assessment High-level Events and Situations Alert Alert Generator High-level Situation Events High-level Situation Assessor Events

19. Recommendations Arguments Decision Options Events and Situation Types Decision-Aiding in COGENTResponse Recommendation • Function • To recommend a suitable action based on the current situation • Internal Architecture Aggregation Argumentation Decision Option Generator

20. Decision-Aiding in COGENTResponse Recommendation • Key aspect of COGENT’s decision-aiding process • Ability to explain the user the system output by following the steps of arguments • COGENT provides justifications for its decision recommendations • Data sources • Knowledge

21. Evaluation and Implementation • COGENT has been implemented in a real-life situation to help humans take decisions:     • Filters raw data according to predefined profiles by parsing telemetry messages • Summarizes results using a library of statistical and clustering methods • Prioritizes summaries and maps different priorities to different format to help visualization • Assesses situation thanks to belief network algorithms • Recommends response using a Prolog platform

22. Conclusions of this study • COGENT relies on complementary AI techniques to support processing at three levels • Data preparation • Data fusion • Information filtering • Summarization • Situation assessment • Belief nets • Response recommendation • Argumentation

23. Objectives • To present a cognitive agent, COGENT, to amplify human perception and cognition • Motivation • To provide decision-aiding at multiple levels of information processing (spacecraft operations) in a manner that complements existing human perceptual and cognitive abilities in tasks that humans find difficult • Management of uncertain information • Keeping track of large numbers of details • Detecting conflicts among currently active alternatives • Etc. • Goal of this paper • To illustrate how COGENT integrates several techniques to successfully support alternating reasoning types of the human operator

24. Future Directions • Candidate research directions for further improvements:             • Use of Fuzzy Logic to simulate human-like reasoning and handling of uncertainty for data preparation        • Belief Networks do not integrate temporal factor • Use of Dynamic Belief Networks will take into account time-varying status information          • Ability to categorize events according to their level of probability    • Visualization of both outcome and structure of arguments

25. CONCLUSION • A decision-aiding agent using the Rasmussen's model of human thinking is feasible, e.g. COGENT                • filtering information      • sorting and summarizing data              • proposing solutions • However, human judgment cannot be replaced: the final decision is still and must be taken by the operator

26. Comments & Questions Thank you very much !