Christian Lebiere

NeSy Conference 2024 Conference Paper

Metacognitive AI: Framework and the Case for a Neurosymbolic Approach

• Hua Wei 0001
• Paulo Shakarian
• Christian Lebiere
• Bruce A. Draper
• Nikhil Krishnaswamy
• Sergei Nirenburg

Abstract Metacognition is the concept of reasoning about an agent’s own internal processes and was originally introduced in the field of developmental psychology. In this position paper, we examine the concept of applying metacognition to artificial intelligence. We introduce a framework for understanding metacognitive artificial intelligence (AI) that we call TRAP: transparency, reasoning, adaptation, and perception. We discuss each of these aspects in-turn and explore how neurosymbolic AI (NSAI) can be leveraged to address challenges of metacognition.

AAMAS Conference 2019 Conference Paper

The Matrix: An Agent-Based Modeling Framework for Data Intensive Simulations

• Parantapa Bhattacharya
• Saliya Ekanayake
• Chris J. Kuhlman
• Christian Lebiere
• Don Morrison
• Samarth Swarup
• Mandy L. Wilson
• Mark G. Orr

Human decision-making is influenced by social, psychological, neurological, emotional, normative, and learning factors, as well as individual traits like age and education level. Social/cognitive computational models that incorporate these factors are increasingly used to study how humans make decisions. A result is that agent models, within agent-based modeling (ABM), are becoming more heavyweight, i. e. , are more computationally demanding, making scalability and at-scale simulations all the more difficult to achieve. To address these challenges, we have developed an ABM simulation framework that addresses data-intensive simulation at-scale. We describe system requirements and design, and demonstrate atscale simulation by modeling 3 million users (each as an individual agent), 13 million repositories, and 239 million user-repository interactions on GitHub. Simulations predict user interactions with GitHub repositories, which, to our knowledge, are the first simulations of this kind. Our simulations demonstrate a three-order of magnitude increase in the number of cognitive agents simultaneously interacting.

AAMAS Conference 2019 Conference Paper

Warning Time: Optimizing Strategic Signaling for Security Against Boundedly Rational Adversaries

• Sarah Cooney
• Phebe Vayanos
• Thanh H. Nguyen
• Cleotilde Gonzalez
• Christian Lebiere
• Edward A. Cranford
• Milind Tambe

Defender-attacker Stackelberg security games (SSGs) have been applied for solving many real-world security problems. Recent work in SSGs has incorporated a deceptive signaling scheme into the SSG model, where the defender strategically reveals information about her defensive strategy to the attacker, in order to influence the attacker’s decision making for the defender’s own benefit. In this work, we study the problem of signaling in security games against a boundedly rational attacker.

IS Journal 2017 Journal Article

Interactive Task Learning

• John E. Laird
• Kevin Gluck
• John Anderson
• Kenneth D. Forbus
• Odest Chadwicke Jenkins
• Christian Lebiere
• Dario Salvucci
• Matthias Scheutz

This article presents a new research area called interactive task learning (ITL), in which an agent actively tries to learn not just how to perform a task better but the actual definition of a task through natural interaction with a human instructor while attempting to perform the task. The authors provide an analysis of desiderata for ITL systems, a review of related work, and a discussion of possible application areas for ITL systems.

IROS Conference 2016 Conference Paper

Validation of cognitive models for collaborative hybrid systems with discrete human input

• Abraham P. Vinod
• Yuqing Tang 0001
• Meeko M. K. Oishi
• Katia P. Sycara
• Christian Lebiere
• Michael Lewis 0001

We present a method to validate a cognitive model, based on the cognitive architecture ACT-R, in dynamic human-automation systems with discrete human input. We are inspired by the general problem of K-choice games as a proxy for many decision making applications in dynamical systems. We model the human as a Markovian controller based on gathered experimental data, that is, a non-deterministic control input with known likelihoods of control actions associated with certain configurations of the state-space. We use reachability analysis to predict the outcome of the resulting discrete-time stochastic hybrid system, in which the outcome is defined as a function of the system trajectory. We suggest that the resulting expected outcomes can be used to validate the cognitive model against actual human subject data. We apply our method to a two-choice game in which the human is tasked with maximizing net coverage of a robotic swarm that can operate under rendezvous or deployment dynamics. We validate the corresponding ACTR cognitive model generated with the data from eight human subjects. The novelty of this work is (1) a method to compute expected outcome in a hybrid dynamical system with a Markov chain model of the human's discrete choice, and (2) application of this method to validation of cognitive models with a database of actual human subject data.

AAAI Conference 2015 Conference Paper

Toward Mobile Robots Reasoning Like Humans

• Jean Oh
• Arne Suppé
• Felix Duvallet
• Abdeslam Boularias
• Luis Navarro-Serment
• Martial Hebert
• Anthony Stentz
• Jerry Vinokurov

Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, and be able to communicate with human teammates in a clear and natural way. To address these challenges, we have developed an intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in the world. This paper describes recent developments using this architecture on a fielded mobile robot platform operating in unknown urban environments. We report a summary of extensive outdoor experiments; the results suggest that a multidisciplinary approach to robotics has the potential to create competent human-robot teams.

AAAI Conference 2012 Conference Paper

Social Cognition: Memory Decay and Adaptive Information Filtering for Robust Information Maintenance

• David Reitter
• Christian Lebiere

Two information decay methods are examined that help multi-agent systems cope with dynamic environments. The agents in this simulation have human-like memory and a mechanism to moderate their communications: they forget internally stored information via temporal decay, and they forget distributed information by filtering it as it passes through a communication network. The agents play a foraging game, in which performance depends on communicating facts and requests and on storing facts in internal memory. Parameters of the game and agent models are tuned to human data. Agent groups with moderated communication in smallworld networks achieve optimal performance for typical human memory decay values, while non-adaptive agents benefit from stronger memory decay. The decay and filtering strategies interact with the properties of the network graph in ways suggestive of an evolutionary cooptimization between the human cognitive system and an external social structure.

NeSy Conference 2012 Conference Paper

Unsurpervised Learning in Hybrid Cognitive Architectures

• Yury Vinokurov
• Christian Lebiere
• Dean Wyatte
• Seth A. Herd
• Randall C. O'Reilly

We present a model of unsupervised learning in the hybrid SAL (Synthesis of ACT-R and Leabra) architecture. This model follows the hypothesis that higher evaluative cognitive mechanisms can serve to provide training signals for perceptual learning. This addresses the problem that supervised learning seems necessary for strong perceptual performance, but explicit feedback is rare in the real world and difficult to provide for artificial learning systems. The hybrid model couples the perceptual strengths of Leabra with ACT-R’s cognitive mechanisms, specifically its declarative memory, to evolve its own symbolic representations of objects encountered in the world. This is accomplished by presenting the objects to the Leabra visual system and committing the resulting representation to ACT-R’s declarative memory. Subsequent presentations are either recalled as instances of a previous object category, in which case the positive association with the representation is rehearsed by Leabra, or they cause ACT- R to generate new category labels, which are also subject to the same rehearsal. The rehearsals drive the network’s representations to convergence for a given category; at the same time, rehearsals on the ACT-R side reinforce the chunks that encode the associations between representation and label. In this way, the hybrid model bootstraps itself into learning new categories and their associated features; this framework provides a potential approach to solving the symbol grounding problem. We outline the operations of the hybrid model, evaluate its performance on the CU3D-100 (cu3d. colorado. edu) image set, and discuss further potential improvements to the model, including the integration of motor functions as a way of providing an internal feedback signal to augment and guide a purely bottom-up unsupervised system.

AAAI Conference 2012 Conference Paper

Using Expectations to Drive Cognitive Behavior

• Unmesh Kurup
• Christian Lebiere
• Anthony Stentz
• Martial Hebert

Generating future states of the world is an essential component of high level cognitive tasks such as planning. We explore the notion that such future state generation is more widespread and forms an integral part of cognition. We call these generated states expectations, and propose that cognitive systems constantly generate expectations, match them to observed behavior and react when a difference exists between the two. We describe an ACT R model that performs expectation driven cognition on two tasks pedestrian tracking and behavior classification. The model generates expectations of pedestrian movements to track them. The model also uses differences in expectations to identify distinctive features that differentiate these tracks. During learning, the model learns the association between these features and the various behaviors. During testing, it classifies pedestrian tracks by recalling the behavior associated with the features of each track. We tested the model on both single and multiple behavior datasets and compared the results against a k NN classifier. The k NN classifier outperformed the model in correct classifications, but the model had fewer incorrect classifications in the multiple behavior case, and both systems had about equal incorrect classifications in the single behavior case.

ICML Conference 2000 Conference Paper

Achieving Efficient and Cognitively Plausible Learning in Backgammon

• Scott Sanner
• John R. Anderson
• Christian Lebiere
• Marsha C. Lovett

NeurIPS Conference 1989 Conference Paper

The Cascade-Correlation Learning Architecture

• Scott Fahlman
• Christian Lebiere

Cascade-Correlation is a new architecture and supervised learning algo(cid: 173) rithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology. Cascade-Correlation begins with a min(cid: 173) imal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detec(cid: 173) tors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network. determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network. 1 DESCRIPTION OF CASCADE·CORRELATION The most important problem preventing the widespread application of artificial neural networks to real-world problems is the slowness of existing learning algorithms such as back-propagation (or "backprop"). One factor contributing to that slowness is what we call the moving target problem: because all of the weights in the network are changing at once, each hidden units sees a constantly changing environment. Instead of moving quickly to assume useful roles in the overall problem solution, the hidden units engage in a complex dance with much wasted motion. The Cascade-Correlation learning algorithm was developed in an attempt to solve that problem. In the problems we have examined, it learns much faster than back-propagation and solves some other problems as well. The Cascade-Correlation Learning Architecture 525