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Pat Langley

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46

AIJ Journal 2025 Journal Article

Introduction to open-world AI

  • Lawrence Holder
  • Pat Langley
  • Bryan Loyall
  • Ted Senator

Open-world AI is characterized by sudden novel changes in a domain that are outside the scope of the training data, or the deployment of an agent in conditions that violate the implicit or explicit assumptions of the designer. In such situations, the AI system must detect the novelty and adapt in a short time frame. In this introduction to the special issue on open-world AI, we discuss the background and motivation for this new research area and define the field in the context of similar AI challenges. We then discuss recent research in the area that has made significant contributions to the field. Many of those contributions are reflected in the papers of this special issue, which we summarize alongside more traditional approaches to open-world AI. Finally, we discuss future directions for the field.

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AAAI Conference 2025 Conference Paper

Learning Hierarchical Task Knowledge for Planning

  • Pat Langley

In this paper, I review approaches for acquiring hierarchical knowledge to improve the effectiveness of planning systems. First I note some benefits of such hierarchical content and the advantages of learning over manual construction. After this, I consider alternative paradigms for encoding and acquiring plan expertise before turning to hierarchical task networks. I specify the inputs to HTN learners and three subproblems they must address: identifying hierarchical structure, unifying method heads, and finding method conditions. Finally, I pose seven challenges the community should pursue so that techniques for learning HTNs can reach their full potential.

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AAAI Conference 2024 Conference Paper

Integrated Systems for Computational Scientific Discovery

  • Pat Langley

This paper poses the challenge of developing and evaluating integrated systems for computational scientific discovery. We note some distinguishing characteristics of discovery tasks, examine eight component abilities, review previous successes at partial integration, and consider hurdles the AI research community must leap to transform the vision for integrated discovery into reality. In closing, we discuss promising scientific domains in which to test such computational artifacts.

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AAAI Conference 2022 Conference Paper

The Computational Gauntlet of Human-Like Learning

  • Pat Langley

In this paper, I pose a major challenge for AI researchers: to develop systems that learn in a human-like manner. I briefly review the history of machine learning, noting that early work made close contact with results from cognitive psychology but that this is no longer the case. I identify seven characteristics of human behavior that, if reproduced, would offer better ways to acquire expertise than statistical induction over massive training sets. I illustrate these points with two domains - mathematics and driving - where people are effective learners and review systems that address them. In closing, I suggest ways to encourage more research on human-like learning.

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AAAI Conference 2020 Conference Paper

Open-World Learning for Radically Autonomous Agents

  • Pat Langley

In this paper, I pose a new research challenge – to develop intelligent agents that exhibit radical autonomy by responding to sudden, long-term changes in their environments. I illustrate this idea with examples, identify abilities that support it, and argue that, although each ability has been studied in isolation, they have not been combined into integrated systems. In addition, I propose a framework for characterizing environments in which goal-directed physical agents operate, along with specifying the ways in which those environments can change over time. In closing, I outline some approaches to the empirical study of such open-world learning.

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AAAI Conference 2019 Conference Paper

Explainable, Normative, and Justified Agency

  • Pat Langley

In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area.

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AAAI Conference 2017 Conference Paper

Flexible Model Induction through Heuristic Process Discovery

  • Pat Langley
  • Adam Arvay

Inductive process modeling involves the construction of explanatory accounts for multivariate time series. As typically specified, background knowledge is available in the form of generic processes that serve as the building blocks for candidate model structures. In this paper, we present a more flexible approach that, when available processes are insufficient to construct an acceptable model, automatically produces new generic processes that let it complete the task. We describe FPM, a system that implements this idea by composing knowledge about algebraic rate expressions and about conceptual processes like predation and remineralization in ecology. We demonstrate empirically FPM's ability to construct new generic processes when necessary and to transfer them later to new modeling tasks. We also compare its failure-driven approach with a naive scheme that generates all possible processes at the outset. We conclude by discussing prior work on equation discovery and model construction, along with plans for additional research.

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IS Journal 2017 Journal Article

Interactive Cognitive Systems and Social Intelligence

  • Pat Langley

Research on cognitive systems adopts the aims and assumptions of classical AI research, emphasizing the construction of intelligent agents that exhibit complex behavior. This article reviews the cognitive systems paradigm and two widely adopted hypotheses-physical symbol systems and heuristic search-that underpin it. The author introduces a third claim-the social cognition hypothesis-that states intelligence requires the ability to represent and reason about others' mental states. The article also examines a number of computational artifacts, both historical and recent, that focus on interaction and exhibit this capacity. Examples include dialogue systems, synthetic experts, believable agents, intelligent tutors, interactive robots, and instructable game players. In closing, the author identifies issues in social cognition that deserve greater attention and poses challenges that can drive future research on interactive cognitive systems.

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AAAI Conference 2017 Conference Paper

Progress and Challenges in Research on Cognitive Architectures

  • Pat Langley

Research on cognitive architectures attempts to develop uni- fied theories of the mind. This paradigm incorporates many ideas from other parts of AI, but it differs enough in its aims and methods that it merits separate treatment. In this paper, we review the notion of cognitive architectures and some recurring themes in their study. Next we examine the substantial progress made by the subfield over the past 40 years, after which we turn to some topics that have received little attention and that pose challenges for the research community.

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AAAI Conference 2015 Conference Paper

Dialogue Understanding in a Logic of Action and Belief

  • Alfredo Gabaldon
  • Pat Langley

In recent work, Langley et al. (2014) introduced UMBRA, a system for plan and dialogue understanding. The program applies a form of abductive inference to generate explanations incrementally from relational descriptions of observed behavior and knowledge in the form of rules. Although UM- BRA’s creators described the system architecture, knowledge, and inferences, along with experimental studies of its operation, they did not provide a formalization of its structures or processes. In this paper, we analyze both aspects of the architecture in terms of the Situation Calculus—a classical logic for reasoning about dynamical systems—and give a specification of the inference task the system performs. After this, we state some properties of this formalization that are desirable for the task of incremental dialogue understanding. We conclude by discussing related work and describing our plans for additional research.

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AAAI Conference 2014 Conference Paper

Social Planning: Achieving Goals by Altering Others’ Mental States

  • Chris Pearce
  • Ben Meadows
  • Pat Langley
  • Mike Barley

In this paper, we discuss a computational approach to the cognitive task of social planning. First, we specify a class of planning problems that involve an agent who attempts to achieve its goals by altering other agents’ mental states. Next, we describe SFPS, a flexible problem solver that generates social plans of this sort, including ones that include deception and reasoning about other agents’ beliefs. We report the results for experiments on social scenarios that involve different levels of sophistication and that demonstrate both SFPS’s capabilities and the sources of its power. Finally, we discuss how our approach to social planning has been informed by earlier work in the area and propose directions for additional research on the topic.

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AAAI Conference 2012 Conference Paper

Discovering Constraints for Inductive Process Modeling

  • Ljupco Todorovski
  • Will Bridewell
  • Pat Langley

Scientists use two forms of knowledge in the construction of explanatory models: generalized entities and processes that relate them; and constraints that specify acceptable combinations of these components. Previous research on inductive process modeling, which constructs models from knowledge and time-series data, has relied on handcrafted constraints. In this paper, we report an approach to discovering such constraints from a set of models that have been ranked according to their error on observations. Our approach adapts inductive techniques for supervised learning to identify process combinations that characterize accurate models. We evaluate the method’s ability to reconstruct known constraints and to generalize well to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstruct known modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.

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AAAI Conference 2010 Conference Paper

Integrated Systems for Inducing Spatio-Temporal Process Models

  • Chunki Park
  • Will Bridewell
  • Pat Langley

Quantitative modeling plays a key role in the natural sciences, and systems that address the task of inductive process modeling can assist researchers in explaining their data. In the past, such systems have been limited to data sets that recorded change over time, but many interesting problems involve both spatial and temporal dynamics. To meet this challenge, we introduce SCISM, an integrated intelligent system which solves the task of inducing process models that account for spatial and temporal variation. We also integrate SCISM with a constraint learning method to reduce computation during induction. Applications to ecological modeling demonstrate that each system fares well on the task, but that the enhanced system does so much faster than the baseline version.

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AAAI Conference 2006 Conference Paper

A Unified Cognitive Architecture for Physical Agents

  • Pat Langley

In this paper we describe ICARUS, a cognitive architecture for physical agents that integrates ideas from a number of traditions, but that has been especially influenced by results from cognitive psychology. We review ICARUS’ commitments to memories and representations, then present its basic processes for performance and learning. We illustrate the architecture’s behavior on a task from in-city driving that requires interaction among its various components. In addition, we discuss ICARUS’ consistency with qualitative findings about the nature of human cognition. In closing, we consider the framework’s relation to other cognitive architectures that have been proposed in the literature.

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AIIM Journal 2006 Journal Article

Constructing explanatory process models from biological data and knowledge

  • Pat Langley
  • Oren Shiran
  • Jeff Shrager
  • Ljupčo Todorovski
  • Andrew Pohorille

Objective We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation. Methods We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input. Results We demonstrate IPM’s use both on photosynthesis and on a second domain, biochemical kinetics, reporting the models induced and their fit to observations. Conclusion We consider the generality of our approach, discuss related research on biological modeling, and suggest directions for future work.

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JMLR Journal 2006 Journal Article

Learning Recursive Control Programs from Problem Solving

  • Pat Langley
  • Dongkyu Choi

In this paper, we propose a new representation for physical control -- teleoreactive logic programs -- along with an interpreter that uses them to achieve goals. In addition, we present a new learning method that acquires recursive forms of these structures from traces of successful problem solving. We report experiments in three different domains that demonstrate the generality of this approach. In closing, we review related work on learning complex skills and discuss directions for future research on this topic. [abs] [ pdf ][ bib ] &copy JMLR 2006. ( edit, beta )

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AIJ Journal 1997 Journal Article

Selection of relevant features and examples in machine learning

  • Avrim L. Blum
  • Pat Langley

In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.

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UAI Conference 1995 Conference Paper

Estimating Continuous Distributions in Bayesian Classifiers

  • George H. John
  • Pat Langley

When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.

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UAI Conference 1994 Conference Paper

Induction of Selective Bayesian Classifiers

  • Pat Langley
  • Stephanie Sage

In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that c arries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that involve correlated features without reducing the rate of learning in ones that do not. We report experimental results on six natural domains, including comparisons with decision-tree induction, that support these hypotheses. In closing, we discuss other approaches to extending naive Bayesian classifiers and outline some directions for future research.

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ICAPS Conference 1994 Conference Paper

Reactive and Automatic Behavior in Plan Execution

  • Pat Langley
  • Wayne Iba
  • Jeff Shrager

Much of the work on execution assumes that the agent constantly senses the environment, which lets it respond immediately to errors or unexpected events. In this paper, we argue that this purely reactive strategy is only optimal if sensing is inexpensive, and we formulate a simple model of execution that incorporates the cost of sensing. We present an average-case analysis of this model, which shows that in domains with high sensing cost or low probability of error, a more automatic strategy, one with long intervals between sensing, can lead to less expensive execution. The analysis also shows that the distance to the goal has no effect on the optimal sensing interval. These results run counter to the prevailing wisdom in the planning community, but they promise a more balanced approach to the interleaving of execution and sensing.

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AAAI Conference 1992 Conference Paper

An Analysis of Bayesian Classifiers

  • Pat Langley

In this paper we present an average-case analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noise-free Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting predicted learning curves for artificial domains, and give experimental results on these domains as a check on our reasoning.

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AIJ Journal 1989 Journal Article

Data-driven approaches to empirical discovery

  • Pat Langley
  • Jan M. Zytkow

In this paper we track the development of research in empirical discovery. We focus on four machine discovery systems that share a number of features: the use of data-driven heuristics to constrain the search for numeric laws; a reliance on theoretical terms; and the recursive application of a few general discovery methods. We examine each system in light of the innovations it introduced over its predecessors, providing some insight into the conceptual progress that has occurred in machine discovery. Finally, we reexamine this research from the perspectives of the history and philosophy of science.

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AIJ Journal 1989 Journal Article

Models of incremental concept formation

  • John H. Gennari
  • Pat Langley
  • Doug Fisher

Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling the type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions.

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AAAI Conference 1984 Conference Paper

Automated Cognitive Modeling

  • Pat Langley

In this paper we describe an approach to automating the construction of cognitive process models. We make two psychological assumptions: that cognition can be modeled as a production system, and that cognitive behavior involves search through some problem space. Within this framework, we employ a problem reduction approach to constructing cognitive models,in which one begins with a set of independent, overly general condition-action rules, adds appropriate conditions to each of these rules, and then recombines the more specific rules into a final model. Conditions are determined using a discrimination learning method. which requires a set of positive and negative instances for each rule. These instances are based on inferred solution paths that lead to the same answers as those observed in a human subject. We have implemented ACM, a cognitive modeling system that incorporates these methods and applied the system to error data from the domain of multi-column subtraction problems.

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IJCAI Conference 1983 Conference Paper

Learning Effective Search Heuristics

  • Pat Langley

SAGE. 2 is a production system that improves its search strategies with practice. The program incorporates four different heuristics for assigning credit and blame, and employs a discrimination process to direct its search through the space of move-proposing rules. The system has shown its generality by learning search heuristics in five different task domains. In addition to improving its search behavior on practice problems, SAGE. 2 was able to transfer its expertise to scaled-up versions of a task, and in one case transferred its acquired search strategy to problems with different initial and goal states.

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IJCAI Conference 1983 Conference Paper

Modeling Cognitive Development on the Balance Scale Task

  • Stephanie Sage
  • Pat Langley

In this paper we describe a production system model of children's development on the balance scale task. Starting with a set of rules that makes random predictions, the system iearns from its errors and improves as it gains experience. The transition mechanism is a discrimination process that searches for differences between cases in which correct predictions are made and cases in which errors are made The stages through which the system progresses are very similar to those observed in children, so the model provides an explanation of the observed developmental trends Since the system has no notion of torque, it never acquires the ability to completely predict the balance scale's behavior; however, it is able to learn heuristically useful rules despite its incomplete representation of the environment, much as children do.

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IJCAI Conference 1983 Conference Paper

Three Facets of Scientific Discovery

  • Pat Langley
  • Jan M. Zytkow
  • Gary L. Bradshaw
  • Herbert A. Simon

Scientific discovery is a complex process, and in this paper we consider three of its many facets - discovering laws of qualitative structure, finding quantitative relations between variables, and formulating sfructural models of reactions. We describe three discovery systems - GLAUBER, BACON, and DALTON - thr. t address these three aspects of the scientific process. GLAUBER forms classes of objects based on regularities in qualitative data, and states abstract laws in terms of these classes. BACON includes heuristics for finding numerical laws, for postulating intrinsic properties, and for noting common divisors. DALTON formulates molecular models that account for observed reactions, taking advantage of theoretical assumptions to direct its search if they are available. We show how each of the programs is capable of rediscovering laws or models that were found in the early days of chemistry. Finally, we consider some possble interactions between these systems, and the need for an integrated theory of discovery.

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