560 citations · 3.3k across the 60 of their papers we have counts for
22 papers · 1 filter
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex
Charles Lovering, Jessica Zosa Forde, George Konidaris +2
AlphaZero, an approach to reinforcement learning that couples neural networks and Monte Carlo tree search (MCTS), has produced state-of-the-art strategies for traditional board gam…
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
Michael L. Littman, Ifeoma Ajunwa, Guy Berger +14
In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intel…
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program
Jeff Druce, James Niehaus, Vanessa Moody +2
The advances in artificial intelligence enabled by deep learning architectures are undeniable. In several cases, deep neural network driven models have surpassed human level perfor…
People construct simplified mental representations to plan
Mark K. Ho, David Abel, Carlos G. Correa +3
One of the most striking features of human cognition is the capacity to plan. Two aspects of human planning stand out: its efficiency and flexibility. Efficiency is especially impr…
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models
Michael Fishman, Nishanth Kumar, Cameron Allen +4
A general-purpose planning agent requires an open-scope world model: one rich enough to tackle any of the wide range of tasks it may be asked to solve over its operational lifetime…
The Efficiency of Human Cognition Reflects Planned Information Processing
Mark K. Ho, David Abel, Jonathan D. Cohen +2
Planning is useful. It lets people take actions that have desirable long-term consequences. But, planning is hard. It requires thinking about consequences, which consumes limited c…
Interactive Learning of Environment Dynamics for Sequential Tasks
Robert Loftin, Bei Peng, Matthew E. Taylor +2
In order for robots and other artificial agents to efficiently learn to perform useful tasks defined by an end user, they must understand not only the goals of those tasks, but als…
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
Michael Shum, Max Kleiman-Weiner, Michael L. Littman +1
Human social behavior is structured by relationships. We form teams, groups, tribes, and alliances at all scales of human life. These structures guide multi-agent cooperation and c…
Finding Options that Minimize Planning Time
Yuu Jinnai, David Abel, D Ellis Hershkowitz +2
We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maxim…
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
Lucas Lehnert, Stefanie Tellex, Michael L. Littman
One question central to Reinforcement Learning is how to learn a feature representation that supports algorithm scaling and re-use of learned information from different tasks. Succ…
Modeling Latent Attention Within Neural Networks
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti +3
Deep neural networks are able to solve tasks across a variety of domains and modalities of data. Despite many empirical successes, we lack the ability to clearly understand and int…
Environment-Independent Task Specifications via GLTL
Michael L. Littman, Ufuk Topcu, Jie Fu +3
We propose a new task-specification language for Markov decision processes that is designed to be an improvement over reward functions by being environment independent. The languag…
Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K Ho, Robert Loftin +5
This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has…
An Alternative Softmax Operator for Reinforcement Learning
Kavosh Asadi, Michael L. Littman
A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in set…
On the Complexity of Solving Markov Decision Problems
Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling
Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI researchers studying automated planning and reinforcement learning. In this paper…
The Complexity of Plan Existence and Evaluation in Probabilistic Domains
Judy Goldsmith, Michael L. Littman, Martin Mundhenk
We examine the computational complexity of testing and finding small plans in probabilistic planning domains with succinct representations. We find that many problems of interest a…
Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes
Anthony R. Cassandra, Michael L. Littman, Nevin Lianwen Zhang
Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of…
Learning is planning: near Bayes-optimal reinforcement learning via Monte-Carlo tree search
John Asmuth, Michael L. Littman
Bayes-optimal behavior, while well-defined, is often difficult to achieve. Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it is possible to act near-o…
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions
J. A. Csirik, M. L. Littman, D. McAllester +2
Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding…
ATTac-2000: An Adaptive Autonomous Bidding Agent
M. Kearns, M. L. Littman, S. Singh +1
The First Trading Agent Competition (TAC) was held from June 22nd to July 8th, 2000. TAC was designed to create a benchmark problem in the complex domain of e-marketplaces and to m…
The Computational Complexity of Probabilistic Planning
M. L. Littman, J. Goldsmith, M. Mundhenk
We examine the computational complexity of testing and finding small plans in probabilistic planning domains with both flat and propositional representations. The complexity of pla…
Reinforcement Learning: A Survey
L. P. Kaelbling, M. L. Littman, A. W. Moore
This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the h…