3,131 research outputs found
Soft behaviour modelling of user communities
A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model
Learning Task Specifications from Demonstrations
Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.Comment: NIPS 201
Learning Linear Temporal Properties
We present two novel algorithms for learning formulas in Linear Temporal
Logic (LTL) from examples. The first learning algorithm reduces the learning
task to a series of satisfiability problems in propositional Boolean logic and
produces a smallest LTL formula (in terms of the number of subformulas) that is
consistent with the given data. Our second learning algorithm, on the other
hand, combines the SAT-based learning algorithm with classical algorithms for
learning decision trees. The result is a learning algorithm that scales to
real-world scenarios with hundreds of examples, but can no longer guarantee to
produce minimal consistent LTL formulas. We compare both learning algorithms
and demonstrate their performance on a wide range of synthetic benchmarks.
Additionally, we illustrate their usefulness on the task of understanding
executions of a leader election protocol
A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
Constraint-based pattern discovery is at the core of numerous data mining
tasks. Patterns are extracted with respect to a given set of constraints
(frequency, closedness, size, etc). In the context of sequential pattern
mining, a large number of devoted techniques have been developed for solving
particular classes of constraints. The aim of this paper is to investigate the
use of Constraint Programming (CP) to model and mine sequential patterns in a
sequence database. Our CP approach offers a natural way to simultaneously
combine in a same framework a large set of constraints coming from various
origins. Experiments show the feasibility and the interest of our approach
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