Complex Operational Decision Making in Networked Systems of Humans and Machines A Multidisciplinary Approach (2014) / Chapter Skim
Currently Skimming:
Chapter 5 Enabling Technologies
Chapter 5 Enabling Technologies
Pages 49-60
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 49... ...
Similarly, the Department of Defense acquires significant amounts of images and videos in daily operations, with reports indicating terabytes of data being generated in Iraq in a single day. Both Google and Facebook possess sufficient data for reliable object recognition, even face identification, at levels that rival human performance.
|
From page 50... ...
Such automatic analysis is the only way to deal with massive amounts of data, and the only way to infer hidden information and singularities relevant for decision making. Significant recent examples in this area include, again, the automatic annotation and labeling (object detection)
|
From page 51... ...
Even though distributed computation offers robustness, in that there is no single point of system failure, multiagent systems face a multitude of challenges, including How to formulate the distributed problem, allocate tasks to various agents, and synthesize the results; How to initiate agent interactions, including when and what agents should communicate; How to ensure coherence in the distributed problem solving and avoid harmful interactions and effects; How to enable agents to reason about the state of their overall coordinated process; How to manage allocation of limited resources; How to allow agents to form and maintain a model of the other agents' problem solving so as to coordinate more effectively; How to reconcile conflicting local viewpoints, intentions, information, and results; How to manage distributed problem solving in the face of failures, and changing environmental and social dynamics (e.g., agents unpredictably leave and join the agent society) ; and 7 This definition is adapted from Jennings, Sycara, and Wooldridge, 1998.
|
From page 52... ...
While a large body of research has been conducted for agent decision support of single decision makers, there is comparatively little work on agent assistance for networked human decision-making teams. AGENTS SUPPORTING HUMANS Researchers desire to make agents an integral part of teams (Christoffersen and Woods, 2004)
|
From page 53... ...
Instead of merely assisting human team members, the software agents can assume equal roles in the team, sometimes replacing missing human team members. It can be challenging to develop software agents of comparable competency with human performers unless the task is relatively simple.
|
From page 54... ...
processes information, executes cognition and then takes actions based on that information. Across the animal kingdom, the human brain – with its dense and folded cortex – is seen to be the pinnacle of cognitive evolution.
|
From page 55... ...
Perhaps one day, the computer will join in the networked teams, both collaborating on a task and sensing/optimizing the performance of its human teammates. In additional to purely decision making states, research is now revealing brain states that may account for bias formation and influenceability.
|
From page 56... ...
Similarly, they might extend human memory or provoke "out-of-the-box" thinking. Already there is a robust collection of transcranial direct current stimulation (tDCS)
|
From page 57... ...
A recent report from the National Research Council21 discussed the use of human computation, or crowdsourcing, for data acquisition, noting that "This has already been shown to be a powerful mechanism for tasks as varied as monitoring road traffic, identifying and locating distributed phenomena, and discovering emerging trends and events." It points to tasks such as "deep language understanding and certain kinds of pattern recognition and outlier detection" that can be performed better by people than by machines, and notes a number of emerging opportunities to harness that capability. It goes on to make a distinction between crowdsourcing that leverages human activity, such as by tracking the way humans search for information on the Web or navigate a challenge, and that which leverages human intelligence, such as by enlisting multiple humans to work in parallel to label images or otherwise contribute to content and analyses.
|
From page 58... ...
, general expertise-based sites, where people with expertise in particular topics answer questions on those topics (e.g., Quora) , and specialized sites focused on a particular topic (e.g., StackOverflow for computer-programming related questions)
|
From page 59... ...
. While the area of human computation is still quite new, the wide range of innovation currently emerging seems likely to someday produce results that can be applied to complex decision making.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.