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Mutual Empowerment in Human-Agent-Robot Teams 16 December 2010 HART Workshop Jurriaan van Diggelen Problem statement • Achieve more with less people • Automation can help to: – Make better use of available semi-structured information sources – Support decision makers in dealing with the complexity of problems (war amongst the people) The big number cruncher Sensor data Twitter data Problem solution UAV images • Monolithic approach, BNC replaces existing infrastructure • AI-complete Towards a human-machine team solution • Solution must be provided by a human machine team • Mutual empowerment seeks to improve team performance by: – Compensating weaknesses of humans and machines – Optimizing strengths of humans and machines Types of Mutual Empowerment Intelligent Interfaces human machine CCI Collective Intelligence HMI CMI human HMI machine User empowerment Distributed Artificial Intelligence Goal ME handbook Methodology Domain Exploration •Domain •Human Factors •Technology Validation •Mixed reality validation •Data collection Functional design •Use cases •Claims •Cognitive requirements •Ontologies •Performance measures •Tests/benchmarks Tool support Prototyping •System requirements •Functional modules •RDF interface specifications •Prototypes Situated Cognitive Engineering • Methodology supports – Incremental design – Reuse of earlier work (Prototypes, tests, requirements, use cases) – Collaborative development Example Phase 1: domain exploration • Domain – USAR – UGV, UAV – Operators in field • Human Factors – Maintaining situation awareness – Cognitive overload – Adaptive teams • Technology – Collaborative tagging, crowd sourcing – Mixed initiative systems – Adaptive/ adaptable automation Phase 2: Functional design (1) • Use cases UC 23 • UAV classifies camera image as victim with certainty-level Unsure • Operator of Robot1 is notified of the potential victim and views the camera images • Operator of Robot1 classifies the image as victim with certainty level Certain • Operator of Robot2 is notified about the victim •… • Cognitive requirements CR 5.1 Uncertainty management Operators and agents can publish and change the certainty value of information Use cases: UC 23 • Claims CR 5.1 • + improves situation awareness of operators and agents • - increases cognitive taskload Phase 2: Functional design (2) something • Ontologies action event item robot victim • Performance measures – E.g. situation awareness measure • Tests/benchmarks – Test for evaluating performance Phase 3: Prototyping • Develop system requirements that implement the cognitive requirements. • Bundle system requirements in functional modules. • Reuse existing base platform Trex Trex • Filter: which data do you want to see? selection of semantic tags in Sparql • Projection: How do you want to see the data? graphical object with attachmentpoints for semantic tags Functional modules supported by Trex • • • • • • User configurable information filters User configurable information visualization Realtime semi-structured data exploration Collective relevance assessment Uncertainty management Human-in-the-loop AI Human-in-the-loop AI P Q Human R Machine S Crowd T Machine DEMO Future work • Develop functional modules for: – Joint conflict resolution – Adaptive Interruptiveness – Network awareness – Policy awareness – Capability awareness – Activity awareness Conclusion • Mutual Empowerment library provides a flexible way to – Increase application possibilities of AI – Employ potential of collective intelligence – Reuse and structure our knowledge of human-machine collaboration tools Domain Analysis ies terfaces Use cases Technology Investigation Exploration Human Factors Claims Metrics Tests Cognitive Requirements Core functions System Requirements Functional design Prototyping Prototype al modules Simulation Test participants Empirical results Testing