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Weekly QuEST Discussion Topics and News, 21 Apr

QuEST 21 April 2017

Last week we began an extremely interesting discussion on ‘Autonomy’.  We used our recently written FAQ (frequently asked questions) on the topic where we generated a self-consistent set of definitions to make our discussions on capabilities and capability gaps more precise.

We started with the idea of locking down terms relevant to multi-agent system of systems (SoS) where we might need for these agents collaborating (note this could be humans and machines in the SoS).  Do these agents have to be intelligent?  Do these agents need to communicate meaning? Do they need to communicate knowledge?  What is understanding?  To have that discussion we used:

1.1         Definitions & Foundational Concepts

1.1.1        What is intelligence? What is artificial intelligence?

Intelligence is the ability to gather observations, generate knowledge, and appropriately apply that knowledge to accomplish tasks. Artificial Intelligence (AI) is a machine that possesses intelligence.

1.1.2        What is an Autonomous system’s (AS’s) internal representation?

Current AS’s are programmed to complete tasks using different procedures.  The AS’s internal representation is how the agent structures what it knows about the world, its knowledge (what the AS uses to take observations and generate meaning), how the agent structures its meaning and its understanding.  For example, the programmed model used inside of the AS for its knowledge-base.  The knowledge base can change as the AS acquires more knowledge or as the AS further manipulates existing knowledge to create new knowledge.

1.1.3        What is meaning?  Do machines generate meaning?

Meaning is what changes in an Airman’s or Autonomous System’s (AS’s) internal representation as a result of some stimuli.  It is the meaning of the stimuli to that the Airman or the System. When you, the Airman, look at an American flag, the sequence of thoughts and emotions that it evokes in you, is the meaning of that experience to you at that moment. When the image is shown to a computer, and if the pixel intensities evoked some programed changes in that computers program, then that is the meaning of that flag to that computer (the AS). Here we see that the AS generates meaning that is completely different than what an Airmen does. The change in the AS’s internal representation, as a result of how it is programmed, is the meaning to the AS. The meaning of a stimulus is the agent specific representational changes evoked by that stimulus in that agent.  The update to the representation, evoked by the data, is the meaning of the stimulus to this agent.  Meaning is NOT just the posting into the representation of the data it is all the resulting changes to the representation.  For example, the evoking of tacit knowledge or a modification of the ongoing simulation (consciousness) or even the updating of the agent’s knowledge resulting from the stimuli is included in the meaning of a stimulus to an agent.  Meaning is not static and changes over time.  The meaning of a stimulus is different for a given agent depending on when it is presented to the agent.

1.1.4        What is understanding?  Do machines understand?

Understanding is an estimation of whether an AS’s meaning will result in it acceptably accomplishing a task. Understanding occurs if it raises an evaluating Airman or evaluating AS’s belief that the performing AS will respond acceptably. Meaning is the change in an AS’s internal representation resulting from a query (presentation of a stimulus). Understanding is the impact of the meaning resulting in the expectation of successful accomplishment of a particular task.

1.1.5        What is knowledge?

Knowledge is what is used to generate the meaning of stimuli for a given agent.  Historically knowledge comes from the species capturing and encoding via evolution in genetics, experience by an individual animal or animals via culture communicating knowledge to other members of the same species (culture).  With the advances in machine learning it is a reasonable argument that most of the knowledge that will be generated in the world in the future will be done by machines.

1.1.6        What is thinking? Do machines think?

Thinking is the process used to manipulate an AS’s internal representation; a generation of meaning, where meaning is the change in the internal representation resulting from a stimuli. If an AS can change or manipulate its internal representation, then it can think.

1.1.7        What is reasoning? Do machines reason?

Reasoning is thinking in the context of a task.  Reasoning is the ability to think about what is perceived and the actions to take to complete a task. If the system updates its internal representation, it generates meaning, and is doing reasoning when that thinking is associated with accomplishing a task. If the system’s approach is not generating the required ‘meaning’ to acceptably accomplish the task, it is not reasoning appropriately.

We will start this week by opening up to questions associated with this framework of the FAQ terms relevant to systems consisting of multiple agents (humans and computers).  As the framework is new to many it is prudent to rehash them so we can begin to ‘chunk’ them into common use.  It turns out this alone is a challenge – since it is so easy to lose the relationship between the terms as we will use them.  So we will spend some time attempting to come up with an approach to bringing others up to speed on our use of these terms to facilitate conversations.  We didn’t get to it last week so one of the deliverables out of this week’s conversation / discussion will be a simple to understand example thread to capture where we are in making autonomous systems and what the world will look like when we actually deliver these systems in this simple example thread.

The one example that I’ve recently been using is putting into a hotel room Alexa / Siri / Cortana … and having it be a ubiquitous aid.  For example, handling on-demand the HVAC (temp / air in the room) and the audio visual (channel location / movie options / radio …), local information to include weather / transportation / exercise / eating…  The discussion is not to build the widgets that facilitate the physical / cyber connectivity but building the joint cognitive solutions – that is what is necessary in the Alexa representation to facilitate her to be able to understand a set of request she has not been programmed to accomplish.  This will provide the machinery to move to the main topic.

The major focus again this week is on the expectation that solutions for many of the mission capabilities we seek will require an Agile/autonomous System of systems (ASoS).  Agility in this phrase is meant to capture the dynamic nature of the composition of the SoS as well as the dynamic nature of the range of tasks this SoS needs to accomplish.  This system (made up of both human and computer agents) has to solve the issue of collaboration between its agents.  Collaboration will require inter-agent communication.  We seek to have agile communication versus having to standardize a communication protocol to maintain maximum agility.  We expect agents will join and depart from these collaborations and some of the required mission capabilities will not be pre-defined.  It seems logical that these agents have to be intelligent.  Do we need these agents to be able to share knowledge or meaning or both?  What is required for two agents to be able to share knowledge or meaning?  Where do goals and intent fit in our framework?  The goal of collaboration is to accomplish some task that requires the ASoS have an understanding, meaning associated with expected successful completion of the task.  What is required for multiple agents to collaboratively achieve understanding for a given task?

I have several articles and a string of email threads to help guide the discussion.  One classic stream is associated with how to make automation a team player with human members of a team from Klein:

Ten Challenges for Making
Automation a “Team Player”
in Joint Human-Agent Activity – gary Klein …

  • We propose 10 challenges for making automation into effective “team players” when they interact with people in significant ways. Our analysis is based on some of theprinciples of human-centered computing that we have developed individually and jointly over the years, and is adapted from a more comprehensive examination of common ground and coordination … We define joint activity as an extended set of actionsthat are carried out by an ensemble of people who are coordinating with each other.1,2
  • Joint activity involves at least four basic requirements.
    All the participants must:
  • • Enter into an agreement, which we call a Basic Compact, that the participants intend to work together
  • • Be mutually predictable in their actions
  • • Be mutually directable
  • • Maintain common ground

The discussion we want to have with respect to the Klein article is how to take his challenges and map them to our framework so we can understand where our gaps are particularly troublesome.

Learning Multiagent Communication with Backpropagation
Sainbayar Sukhbaatar
Dept. of Computer Science
Courant Institute, New York University …

  • Many tasks in AI require the collaboration of multiple agents. Typically, thecommunication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks.
  • The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines.
  • In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Emergence of Grounded Compositional Language in Multi-Agent Populations
Igor Mordatch

arXiv:1703.04908v1 [cs.AI] 15 Mar 2017

It Begins: Bots Are Learning to Chat in Their Own Language

Igor Mordatch is working to build machines that can carry on a conversation. That’s something so many people are working on. In Silicon Valley, chatbot is now a bona fide buzzword. But Mordatch is different. He’s not a linguist. He doesn’t deal in the AI techniques that typically reach for language. He’s a roboticist who began his career as an animator. He spent time at Pixar and worked on Toy Story 3, in between stints as an academic at places like Stanford and the University of Washington, where he taught robots to move like humans. “Creating movement from scratch is what I was always interested in,” he says. Now, all this expertise is coming together in an unexpected way

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