Archive for October, 2014

Weekly QuEST Discussion Topics and News, 31 Oct

October 30, 2014 Leave a comment

This week’s discussion will center upon Capt Amerika’s keynote NSSDF talk.  Please see below for title and abstract

Situational Consciousness versus Situational

Awareness – A Requirement for Autonomy?


What is autonomy?  How is it different than automation? What is unique to critters, that facilitates their autonomy that we’ve not yet been able to engineer in machine based systems?  Is it that our machine based solutions do not have all the information that they need to be autonomous?  Is it that our machine solutions don’t understand the meaning of the data/information they are being provided? Is it that our machine solutions can’t project what the data/information is suggesting is going to be occurring in the environment next, projection into the future?  These are the levels of situational awareness.  So is it that our machines don’t have enough situational awareness?  This paper proposes that maximizing situational awareness will NOT lead to autonomous solutions.  Situational awareness requires a valid model be available to account for any stimuli and all  relevant stimuli required for the appropriate model be available.  This paper suggest it is consciousness or at least some engineering approximation to consciousness, situational consciousness, is required for autonomy.

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Weekly QuEST Discussion Topics and News, 24 Oct

October 23, 2014 Leave a comment

This week we are excited to have a presentation from Robert Patterson.  Please see below for a brief summary.

QuEST 24 Oct 2014

Presentation by Robert Patterson

Implicit Cognition in Human-machine systems


In my presentation, I will provide an analysis and review of five large, distinct, scientific literatures on human reasoning and decision making: Dual-Process Theory, Fuzzy-Trace Theory, Naturalistic Decision Making, Heuristics and Biases, and Automatic Processes Theory. Taken together, the results from the analysis and review strongly indicate that intuitive cognition (meaningful situational pattern synthesis and recognition) dominates in human decision making. The implications of this claim for human-machine teaming will be discussed.

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Weekly QuEST Discussion Topics and News, 10 Oct

October 10, 2014 Leave a comment

QuEST 10 Oct 2014

Since the discussion last week on the unexpected query – where we were attempting to define where will QuEST solutions bring value and how to define that – we’ve had a series of virtual interactions – the material below captures a snapshot of the points that we will discuss:

What is an agent?

  • We will use the term agent in a broad manner – to include human and computer agents
  • The common functionality of agents is they have sensors that allow them to capturestimuli from the environment
  • Once the stimuli are captured by an agent’s sensors and brought into the agent that is what we call data (note you cannot say data without saying with respect to which agent)
  • The agent then updates some aspect of its internal representation with that data thus reducing some uncertainty in that representation – so we now call that information (again note that information is agent centric as is data)
  • The agent might generate some action using its effectors or the effectors might just output stimuli into the environment for use by other agents.

What is a query?

  • Let’s define Query – as the act of a stimulus being provided to an agent – the stimulus has characteristics that completely capture the salient axes (keep in mind what is salient in a stimulus is agent centric) of the stimuli – some of those axes are captured by an agent in its conversion of that stimuli into data (agent centric internal representation of the stimuli)
  • We use the term query instead of stimuli to capture the idea that a given agent must take the stimuli and appropriately respond (thus an action)

–     that response may be to just update its representation or not or may actually be taking an action through an agent’ effectors

What is an unexpected query?

  • That is the goal of this discussion to bring some specifics to the idea of anunexpected query – but for now realize we use that term with some localization (maybe with respect to an agent – or maybe with respect to some process within an agent= performing agent – that is an unexpected query could be unexpected to some process within an agent or to the agent or collection of agents as a whole) – it is deemedunexpected to the performing agent and that label ‘unexpected’ is from the perspective of an external ‘evaluating agent’

–     But the point of the word ‘unexpected’ is to capture the idea that a process that takes in stimuli and responds (again could just be updating a representation or could be the response is some action taken) has some assumptions built into its design that may or may not be violated by a given stimulus (and the violation is from the perspective of an external agent)

  • When the assumptions that are key for the acceptable response are violated we will term that stimuli as being unexpected to that process/agent (performing agent) – and the violation is from the perspective of the evaluating agent
  • Note – the ‘unacceptable’ nature of any response is determined from the perspective of some other agent {evaluating agent} / process

–     so where one evaluating agent might take the response to a stimuli as unacceptable another may deem it perfectly acceptable – thus the unexpected nature of a stimuli is agent centric (note an agent – the evaluating agent – different than the one reacting to the stimulus the performing agent)

–     Note the evaluating agent has access to additional stimuli AND also has a model of the performing agent – thus can assess that the performing agent has an unacceptable response from its perspective

So why do we care:

  • So we posit that the type 2 processes (consciousness results from type 2 processing) that are situated and simulation based and include a model for the type 1 processes (that is what intuition provides us – at the conscious level it is our model of the evaluation of the Type 1 system projected into a conscious form) can be evaluation agents and detect UQs for the Type 1 processes
  • I suspect similarly the Type 1 processes might receive inputs from Type 2 stimuli and can possibly update their models – a late set of interactions with colleagues Robert P / Mike Y convince me this is a defendable position
  • Bottom line – a simulation based / situated representation can generate solutions to queries where the solutions don’t have to based on the stimuli – they are based on inferences from the simulation/ situation based representation – and from the evaluation agent called evolution perspective that provided more acceptable responses in leading to more reproduction …

What is consciousness?

  • Stable, consistent and useful situated simulation that is structurally coherent

–     The space for the unexpected query for such a representation complements the experiential representation which is the focus of Type 1 processes

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Weekly QuEST Discussion Topics and News, 3 Oct

October 2, 2014 Leave a comment

QuEST 3 Oct 2014:

1.)  We want to spend some bandwidth this week reviewing the questions asked by Mike R / Sandy V on formulating qualia.  It will also provide us as a group an opportunity to discuss any fall-out / vectors associated with our colleague Sandy’s dissertation prospectus defense.  Her abstract: Thisprospectus  examines how a computational model of a Qualia Generating Agent (QGA) can be applied to decisionsupport and AI systems for improved deci- sion making. The principal goal of the research is to develop the QGA ina Cognitive Modeling Architecture (CMA) to improve decision making in simulated event-driven environments andprovide a fundamental cognitive  component  for further research in computational modeling of narratives. Thespecific problem focus is to develop a computational formalism that allows a CMA to store, recall and process withagent- centric, context-dependent, conceptual knowledge, i.e., qualia, more in line with human cognition.  Themethodology will  model and simulate Stanovich’s Tripartite Framework, capture stimuli as episodic memory, aggregate and relate episodic memory into conceptual  knowledge and context, model working memory, andsimulate cognitive decoupling and mental simulation in working memory to compute a set of best explanations based on an abductive process.

2.)  Sandy led me to an article ‘ the importance of cognitive architectures: an analysis based on CLARION’ by Ron Sun.  Abstract: Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad, multiple-level, multiple-domain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will be undertaken, and an example cognitive architecture (CLARION) will be described.

3.)  These discussions led us last week to discuss how our view of the Link game can/should play in implementations – we might review those ideas to help the discussion – our previous discussion along this axes led us to talk about the memory competitions and how people create elaborate memory palaces to ‘store’ away the data.

4.)  Those discussions lead us back to what do we expect is the value added for QuEST solutions – What are the axes for improvement in performance for QuEST solutions – is it accuracy or in reduction in computational resources necessary or is it the ability to respond to the unexpected query – For a set of processes that are fragile and designed to provide very specific information – how can a conscious representation take those building blocks and construct a flexible response mechanism to respond to the unexpected query.  To help stimulate thoughts along these lines I reviewed this week a prior discussion we’ve had associated with ‘Good judgments do not require complex cognition’ v2 by Julian N. Marewski • Wolfgang Gaissmaier • Gerd Gigerenzer — in that work they make the point that – Widespread belief in psychology and beyond that complex judgment tasks require complex solutions.  Countering this common intuition, in this article, we argue that in an uncertain world actually the opposite is true – although the article uses this point to suggest this is the purpose of Type 1 processes (heuristics) I think we can use the same argument to suggest the bit reduced (from a reality perspective) conscious part of the internal representation uses this approach

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