Towards a cognitive approach to human–machine cooperation in dynamic situations

Hoc, Jean‐Michel

doi:10.1006/ijhc.2000.0454

Cited by 289 publications

(158 citation statements)

References 64 publications

Supporting

Mentioning

107

Contrasting

Unclassified

Order By: Relevance

“…This failure has been termed complacency (Hoc, 2001;Parasuraman, Molloy, & Singh, 1993;Smith, McCoy, & Layton, 1997) and has been mainly studied in supervision tasks that imply automation or computers (for example, car driving: Hoc et al, 2006;airline pilots: Layton, Smith, & McCoy, 1994; air traffic control: Metzger & Parasuraman, 2001). However, other tasks that are only partially automated, such as those found in manufacturing scheduling, are also affected by this problem.…”

Section: Introductionmentioning

confidence: 99%

The role of algorithm and result comprehensibility of automated scheduling on complacency

Cegarra¹,

Hoc²

2008

Hum Ftrs & Erg Mfg Svc

Self Cite

View full text Add to dashboard Cite

Several studies have stressed that even expert operators who are aware of a machine's limits could adopt its proposals without questioning them (i.e., the complacency phenomenon). In production scheduling for manufacturing, this is a significant problem, as it is often suggested that the machine be allowed to build the production schedule, confining the human role to that of rescheduling. This article evaluates the characteristics of scheduling algorithms on human rescheduling performance, the quality of which was related to complacency. It is suggested that scheduling algorithms be characterized as having result comprehensibility (the result respects the scheduler's expectations in terms of the discourse rules of the information display) or algorithm comprehensibility (the complexity of the algorithm hides some important constraints). The findings stress, on the one hand, that result comprehensibility is necessary to achieve good production performance and to limit complacency. On the other hand, algorithm comprehensibility leads to poor performance due to the very high cost of understanding the algorithm. C 2008 Wiley Periodicals, Inc.

show abstract

Section: Introductionmentioning

confidence: 99%

The role of algorithm and result comprehensibility of automated scheduling on complacency

Cegarra¹,

Hoc²

2008

Hum Ftrs & Erg Mfg Svc

Self Cite

View full text Add to dashboard Cite

show abstract

“…SharedPlan theory fits naturally to the modeling of intentional structures of collaborative discourse [45] and has been demonstrated by the Collagen system [65] to be a successful model for collaborative, mixed-initiative dialogues. Again, these theories and agent architectures have been partially tested in 'toy' applications, and we expect that crisis management systems require fuller implementation of the notion of cooperative problem-solving [2,9,37].…”

Section: From Queries To Dialogue-assisted Interactionsmentioning

confidence: 99%

Human-GIS interaction issues in crisis response

Cai¹,

Sharma

MacEachren

et al. 2006

IJRAM

View full text Add to dashboard Cite

Abstract. Geospatial information systems (GIS) provide a central infrastructure forcomputer supported crisis management in terms of database, analytical models and visualization tools, but the user interfaces of such systems are still hard to use, and do not address the special needs of crisis managers who often work in teams and make judgments and decisions under stress. This paper articulates the overall challenges for effective GIS interfaces to support crisis management in three dimensions: immediacy, relevancy, and sharing. These three requirements are addressed by an integrated approach, taking a human-GIS interaction perspective. To demonstrate the feasibility of this approach, we cite our prototype system, DAVE_G (Dialogue-Assisted Visual Environment for Geoinformaton), as an example. DAVE_G uses a large screen display to create a shared workspace among team members, and allows risk managers to interact with a GIS through natural multimodal (speech/gesture) dialogues. This work highlights the design challenges and the required technical innovations towards the goal of making geographical information accessible to crisis management teams.Keywords: geographical information systems, human-computer interaction, multimodal interface, speech and gesture, dialogue management, geovisualization Bibliographical notes: Guoray Cai is Assistant Professor of Information Sciences andTechnology with the school of Information Sciences and Technology at the Pennsylvania State University. He is the director of the Spatial Information and Intelligence Laboratory, and an affiliate faculty of GeoVISTA center at Penn State. His research interests include spatial information retrieval, multimodal interfaces for GIS, conversational dialogue systems, and map-mediated communication and collaboration. He is a member of ACM, IEEE, and AAG (American Association of Geographers). Representation, Visualization, and Design (New York, Guilford, 1995). Dr. MacEachren is currently Chair of the International Cartographic Association Commission on Visualization and Virtual Environments. Rajeev Sharma is an Associated Professor of Computer Science and Engineering atIsaac Brewer is a Ph.D. degree candidate at the Department of Geography and the GeoVISTA Center, Pennsylvania State University, University Park. His dissertation research is focused on the development of a cognitive systems engineering approach to guide design of advanced human-computer interfaces that support collaborative actions with a GIS in emergency management situations. His long-term research interests are in the modeling of human cognitive processes and visual systems as it relates to the design of new interface technologies for geographical information.

show abstract

“…However, when one or more of these common agents is a machine it is not clear if, and how, the machine agent maintains a representation of the overall common goal. Noting the asymmetric nature of human-machine cooperation, Hoc (2001) has defined cooperation as: Two agents are in a cooperative situation if they meet two minimal conditions.…”

Section: Cooperationmentioning

confidence: 99%

Dynamic task allocation: Issues for implementing adaptive intelligent automation

Sherry¹,

Ritter²

2002

PsycEXTRA Dataset

View full text Add to dashboard Cite

Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S) SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release; distribution unlimited SUPPLEMENTARY NOTESThe original document contains color images. ABSTRACTHow should tasks be allocated dynamically between people and intelligent machines? What are the initial issues? Previous work on static task allocation and work on human performance when multi-tasking and when interrupted provides suggestions on how to dynamically allocate tasks between humans and machines. We use these results to explore previous theories of task allocation. Some of these theories have direct suggestions for dynamic task allocation and some have indirect implications. We use both types to provide a list of suggestions for creating systems that do dynamic task allocation. The context we will be working with is a type of pilot's associate that has a description of the pilot's tasks and flight mission built within a cognitive architecture. The proposed associate has an additional component that can match the pilot's performance to these tasks, predict the flight phase and pilot's current tasks, and use this information to dynamically allocate these tasks between the pilot and the automation. These suggestions are to inform the design of a high-level intelligent controller. AbstractHow should tasks be allocated dynamically between people and intelligent machines? What are the initial issues? Previous work on static task allocation and work on human performance when multi-tasking and when interrupted provides suggestions on how to dynamically allocate tasks between humans and machines. We use these results to explore previous theories of task allocation. Some of these theories have direct suggestions for dynamic task allocation and some have indirect implications. We use both types to provide a list of suggestions for creating systems that do dynamic task allocation. The context we will be working with is a type of pilot's associate that has a description of the pilot's tasks and flight mission built within a cognitive architecture. The proposed associate has an additional component that can match the pilot's perfor...

show abstract

Towards a cognitive approach to human–machine cooperation in dynamic situations

Cited by 289 publications

References 64 publications

The role of algorithm and result comprehensibility of automated scheduling on complacency

The role of algorithm and result comprehensibility of automated scheduling on complacency

Human-GIS interaction issues in crisis response

Dynamic task allocation: Issues for implementing adaptive intelligent automation

Contact Info

Product

Resources

About