30,720 research outputs found
Discovering Valuable Items from Massive Data
Suppose there is a large collection of items, each with an associated cost
and an inherent utility that is revealed only once we commit to selecting it.
Given a budget on the cumulative cost of the selected items, how can we pick a
subset of maximal value? This task generalizes several important problems such
as multi-arm bandits, active search and the knapsack problem. We present an
algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween
items, expressed as a kernel function. GP-Select uses Gaussian process
prediction to balance exploration (estimating the unknown value of items) and
exploitation (selecting items of high value). We extend GP-Select to be able to
discover sets that simultaneously have high utility and are diverse. Our
preference for diversity can be specified as an arbitrary monotone submodular
function that quantifies the diminishing returns obtained when selecting
similar items. Furthermore, we exploit the structure of the model updates to
achieve an order of magnitude (up to 40X) speedup in our experiments without
resorting to approximations. We provide strong guarantees on the performance of
GP-Select and apply it to three real-world case studies of industrial
relevance: (1) Refreshing a repository of prices in a Global Distribution
System for the travel industry, (2) Identifying diverse, binding-affine
peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale
recommender system by recommending items to users
Learning Classical Planning Strategies with Policy Gradient
A common paradigm in classical planning is heuristic forward search. Forward
search planners often rely on simple best-first search which remains fixed
throughout the search process. In this paper, we introduce a novel search
framework capable of alternating between several forward search approaches
while solving a particular planning problem. Selection of the approach is
performed using a trainable stochastic policy, mapping the state of the search
to a probability distribution over the approaches. This enables using policy
gradient to learn search strategies tailored to a specific distributions of
planning problems and a selected performance metric, e.g. the IPC score. We
instantiate the framework by constructing a policy space consisting of five
search approaches and a two-dimensional representation of the planner's state.
Then, we train the system on randomly generated problems from five IPC domains
using three different performance metrics. Our experimental results show that
the learner is able to discover domain-specific search strategies, improving
the planner's performance relative to the baselines of plain best-first search
and a uniform policy.Comment: Accepted for ICAPS 201
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
Greedy heuristics may be attuned by looking ahead for each possible choice,
in an approach called the rollout or Pilot method. These methods may be seen as
meta-heuristics that can enhance (any) heuristic solution, by repetitively
modifying a master solution: similarly to what is done in game tree search,
better choices are identified using lookahead, based on solutions obtained by
repeatedly using a greedy heuristic. This paper first illustrates how the Pilot
method improves upon some simple well known dispatch heuristics for the
job-shop scheduling problem. The Pilot method is then shown to be a special
case of the more recent Monte Carlo Tree Search (MCTS) methods: Unlike the
Pilot method, MCTS methods use random completion of partial solutions to
identify promising branches of the tree. The Pilot method and a simple version
of MCTS, using the -greedy exploration paradigms, are then
compared within the same framework, consisting of 300 scheduling problems of
varying sizes with fixed-budget of rollouts. Results demonstrate that MCTS
reaches better or same results as the Pilot methods in this context.Comment: Learning and Intelligent OptimizatioN (LION'6) 7219 (2012
Monte Carlo Approaches to Parameterized Poker Squares
The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques
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