Learning Chordal Markov Networks via Branch and Bound

Neural Information Processing Systems

We present a new algorithmic approach for the task of finding a chordal Markov network structure that maximizes a given scoring function. The algorithm is based on branch and bound and integrates dynamic programming for both domain pruning and for obtaining strong bounds for search-space pruning. Empirically, we show that the approach dominates in terms of running times a recent integer programming approach (and thereby also a recent constraint optimization approach) for the problem. Papers published at the Neural Information Processing Systems Conference.

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

Journal of Artificial Intelligence Research

This article describes REBA, a knowledge representation and reasoning architecture for robots that is based on tightly-coupled transition diagrams of the domain at two different levels of granularity. An action language is extended to support non-boolean fluents and non-deterministic causal laws, and used to describe the domain's transition diagrams, with the fine-resolution transition diagram being defined as a refinement of the coarse-resolution transition diagram. The coarse-resolution system description, and a history that includes prioritized defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action.


AAAI Conferences

We argue that this approach is impossible to follow in many real-world domains. The agent may not have enough information to ensure that an action will have a given effect in advance of executing it. This paper describes PUCCINI, a partialorder planner used to control the Internet Softbot (Etzioni & Weld 1994).

High-Level Planning and Control with Incomplete Information Using POMDP's

AAAI Conferences

Some of the features that distinguish this framework from related decisiontheoretic approaches to planning such as (Kushmerick, Hanks, Weld 1995; Draper, Hanks, & Weld 1994; Boutilier, Dean, & Hanks 1995) are: a non-propositional action description language a language for obser, ations that allows us to say 5Thls is because the RTDP-BEL controller uses the large bowl as a'buffer' when it's empty. In that way, half of the time it saves a step over the handcrafted controller.

A Probabilistic Model of Social Decision Making based on Reward Maximization

Neural Information Processing Systems

A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We employ the framework of partially observable Markov decision processes (POMDPs) to model human decision making in a social context, focusing specifically on the volunteer's dilemma in a version of the classic Public Goods Game. We show that the POMDP model explains both the behavior of subjects as well as neural activity recorded using fMRI during the game. The decisions of subjects can be modeled across all trials using two interpretable parameters.