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Analyzer:. Discourse & Situational Context. Lectures I. Overview 2. Simulation Semantics 3. ECG and Best-fit Analysis 4. Compositionality 5. Simulation, Counterfactuals, and Inference. Constructions. Utterance. incremental, competition-based, psychologically plausible.

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Constructions

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Constructions

Analyzer:

Discourse & Situational Context

LecturesI. Overview2. Simulation Semantics3. ECG and Best-fit Analysis4. Compositionality5. Simulation, Counterfactuals, and Inference

Constructions

Utterance

incremental,

competition-based, psychologically plausible

Semantic Specification:

image schemas, bindings, action schemas

Simulation


Evidence for simulation semantics

Evidence for Simulation Semantics

  • BASIC ASSUMPTION: SAME REPRESENTATION FOR PLANNING AND SIMULATIVE INFERENCE

    • Evidence for common mechanisms for recognition and action (mirror neurons) in the F5 area (Rizzolatti et al (1996), Gallese 96, Boccino 2002) and from motor imagery (Jeannerod 1996)

  • IMPLEMENTATION:

    • x-schemas affect each other by enabling, disabling or modifying execution trajectories. Whenever the CONTROLLERschema makes a transition it may set, get, or modify stateleading to triggering or modificationof other x-schemas. State is completely distributed (a graph marking) over the network.

  • RESULT: INTERPRETATION IS IMAGINATIVE SIMULATION!


Active representations

walker at goal

energy

walker=Harry

goal=home

Active representations

  • Many inferences about actions derive from what we know about executing them

  • X-net representation based on stochastic Petri nets captures dynamic, parameterized nature of actions

  • Used for acting, recognition, planning, and language

  • Walking: bound to a specific walker with a direction or goal

  • consumes resources (e.g., energy)

  • may have termination condition(e.g., walker at goal)

  • ongoing, iterative action


Constructions

Task

  • Interpret simple discourse fragments/blurbs

    • France fell into recession. Pulled out by Germany

    • Economy moving at the pace of a Clinton jog.

    • US Economy on the verge of falling back into recession after moving forward on an anemic recovery.

    • Indian Government stumbling in implementing Liberalization plan.

    • Moving forward on all fronts, we are going to be ongoing and relentless as we tighten the net of justice.

    • The Government is taking bold new steps. We are loosening the stranglehold on business, slashing tariffs and removing obstacles to international trade.


Basic result

Basic Result

  • A neurally plausible, computational model and an implementation that is able to cash out the observation that motion, manipulation and spatial concepts are used to convey important and subtle information about abstract domains such as International Economics.

    • In 1991, India set out on a path of Liberalization. After making rapid strides in the first few years, the Government policy hit a first of a series of roadblocks in 1995. By 1998, the new BJP Government had reoriented the Government’s policy ..


I o as feature structures

Indian Government stumbling in implementing liberalization plan

I/O as Feature Structures


Basic primitives

Basic Primitives

  • An fine-grained executing model of action and events (X-schemas)

  • A factorized representation of state (DBN’s)

  • A model of metaphor maps that project bindings from source to target domains.


Features of representation

Features of Representation

  • Inherently action based, with fine grained distinctions in resource usage, and temporal evolutions.

  • Can deal with concurrent actions, durations, hierarchical action sets, and stochastic actions (selection and effects).

  • Highly responsive to a changing environment with uncertain evolutions.

  • Can model complex domain constraints in a factorized representationthat can compute complex ramificationsas well as prior beliefs and possible predictions.


The target domain

The Target Domain

  • Simple knowledge about Economics

    • Factual (US is a market economy)

    • Correlational (High Growth => High Inflation)

  • Key Requirement:

    • Must combine background knowledge of economics with inherent structure and constraints of the target domain with inferential products of metaphoric (and other) projections from multiple source domains.

    • Must be able to compute the global impact of new observations (from direct input as well as metaphoric inferences)


Metaphor maps

Metaphor Maps

  • Static Structures that project bindings from source domain f- struct to target domain Belief net nodes by setting evidence on the target network.

  • Different types of maps

    • PMAPS project X- schema Parameters to abstract domains

    • OMAPS connect roles between source and target domain

    • SMAPS connect schemas from source to target domains.

  • ASPECT is an invariant in projection.


Results

Results

  • Model was implemented and tested on discourse fragments from a database of 30 newspaper stories in international economics from standard sources such as WSJ, NYT, and the Economist. Results show that motion terms are often the most effective method to provide the following types of information about abstract plans and actions.

    • Information about uncertain events and dynamic changes in goals and resources. (sluggish, fall, off-track, no steam)

    • Information about evaluations of policies and economic actors and communicative intent (strangle-hold, bleed).

    • Communicating complex, context-sensitive and dynamic economic scenarios (stumble, slide, slippery slope).

    • Commincating complex event structure and aspectual information (on the verge of, sidestep, giant leap, small steps, ready, set out, back on track).

  • ALL THESE BINDINGS RESULT FROM REFLEX, AUTOMATIC INFERENCES PROVIDED BY X-SCHEMA BASED INFERENCES.


States are dbn

States are DBN

  • Dynamic Bayesian Networks (D(T)BNs) are an extension of Bayesian networks for modeling dynamic systems.

    • In a DBN, the state at time t is represented by a set of random variables. The state at time t is dependent on the states at previous time steps.

    • Typically, we assume that each state only depends on the immediately preceding state (first-order Markovian), and thus we need to represent the transition distribution P(Zt+1 | Zt).

  • This can be done using a two-time-slice Bayesian network fragment (2-TBN) Bt+1,

    • variables from Zt+1 whose parents are variables from Ztand/or Zt+1, and variables from Ztwithout any parents.

    • Typically, we also assume that the process is stationary, i.e., the transition models for all time slices are identical:


An active model of events

An Active Model of Events

  • Computationally, actions and events are coded in active representations called x-schemas which are extensions to Stochastic Petri nets.

  • x-schemas are fine-grained action and event representations that can be used for monitoring and control as well as for inference.


Constructions

Preconditions, resources, fine control structure are important aspects of events


X schema extensions to petri nets

X-schema Extensions to Petri Nets


A walk x schema

A Walk X-schema


Logical action theories

Logical Action Theories

  • Connection to ARD (or other Action Languages):

    • The representation can be used to encode a causal modelfor a domain description D (in the Syntax of ARD) in that it satisfies all the causal lawsin D. Furthermore, a value propositionof the form C after A is entailed by Diff all the terms in C are in Si; the state that results after running the projection algorithmon the action set A. (IJCAI 99)

  • Executing representation,

    • frame axioms are encoded in the topology of the network and transition firing rules respect them.

  • Planning as backward reachability or computing downward closure (IJCAI 99, WWW2002)

  • Links to linear logic. Perhaps a model of stochastic linear logic? (SRI CSL TR 2001).


Event structure in language

Event Structure in Language

  • Fine-grained

  • Rich Notion of Contingency Relationships.

    • Phenomena: Aspect, Tense, Force-dynamics, Modals, Counterfactuals

  • Event Structure Metaphor:

    • Phenomena: Abstract Actions are conceptualized in Motion and Manipulation terms. Invariants in projection.


A climb x schema

A Climb X-schema


A schema controller

A Schema Controller

iterate

Ready

Start

Process

Finish

Done

interrupt

resume

Cancel

Suspend

  • An active controller that sends signals to the embedded schema and transitions based on signals from the embedded schema.

  • Useful for higher level coordination of actions.


A generic process schema

A Generic Process Schema

iterate

Ready

Start

Process

Finish

Done

interrupt

resume

Cancel

Suspend

  • Part of Conceptual Structure.

  • Generalizes over actions and events. Has internal state and models evolution of processes.


About to climb prospective

Hold

Find hold

Stabilize

Pull(self)

About to + (Climb) (Prospective)

Iterate

Ready

Start

Process

Finish

Done

resume

interrupt

Suspend

Cancel

BINDINGS

Energy

Ready

Standing

On top


Be climb ing progressive

Hold

Find hold

Stabilize

Pull(self)

Be + (Climb)-ING (Progressive)

Iterate

Ready

Start

Process

Finish

Done

resume

interrupt

Suspend

Cancel

BINDINGS

Energy

Ready

Standing

On top


Have climb ed perfect

Iterate

Ready

Start

Process

Finish

Done

resume

interrupt

Suspend

Cancel

BINDINGS

Hold

Find hold

Stabilize

Pull(self)

Have + (Climb)-ed (Perfect)

Energy

Ready

Standing

On top


Phasal aspect maps to the controller

Phasal Aspect Maps to the Controller

Iterative (repeat)

Inceptive (start, begin)

Iterate

Ready

Start

Process

Finish

Done

interrupt

resume

Cancel

Suspend

Completive (finish, end)

Resumptive(resume)


A precise notion of contingency relations

A Precise Notion of Contingency Relations

Activation:

Executing one schema causes the enabling, start or continued execution of another schema. Concurrent and sequential activation.

Inhibition:

Inhibitory links prevent execution of the inhibited x-schema by activating an inhibitory arc. The model distinguishes between concurrent and sequential inhibition, mutual inhibition and aperiodicity.

Modification:

The modifying x-schema results in control transition of the modified xschema. The execution of the modifying x-schema could result in the

interruption, termination, resumption of the modified x-schema.


Constructions

SHRUTI: a connectionist cognitive architecture

relational predicate

type

entity

Connectionist model of knowledge representation and reasoning-- and a useful modeling framework. 1. Representation via focal clusters2. Temporal synchrony variable binding


Resources and actions

Resources and actions


Basic features

Basic Features

  • Fine grained model of actions and events

    • Interruption, hierarchy, concurrency, synchronization, iteration

  • Models resources, preconditions, state changes

  • Active representation

  • Feedback loops

    • Forward and backward

    • Extensions allow hybrid system models


Probabilistic relation inference

Probabilistic Relation Inference

  • Scalable Representation of

    • States, domain knowledge, ontologies

      • (Avi Pfeffer 2000, Koller et al. 2001)

  • Merges relational database technolgy with Probabilistic reasoning based on Graphical Models.

    • Domain entities and relational entities

    • Inter-entity relations are probabilistic functions

    • Can capture complex dependencies with both simple and composite slot (chains).

  • Inference exploits structure of the domain


States

States

  • Factorized Representation of State uses Dynamic Belief Nets (DBN’s)

    • Probabilistic Semantics

    • Structured Representation


Shruti

SHRUTI

  • SHRUTI does inference by connections between simple computation nodes

  • Nodes are small groups of neurons

  • Nodes firing in sync reference the same object


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