15 research outputs found
Faster Constraint Solving Using Learning Based Abstractions
This work addresses the problem of scalable constraint solving. Our
technique combines traditional constraint-solving approaches with
machine learning techniques to propose abstractions that simplify the
problem. First, we use a collection of heuristics to learn sets of constraints
that may be well abstracted as a single, simpler constraint. Next, we
use an asymmetric machine learning procedure to abstract the set of clauses, using
satisfying and falsifying instances as training data. Next, we solve a
reduced constraint problem to check that the learned formula is indeed a
consequent (or antecedent) of the formula we sought to abstract, and
finally we use the learned formula to check the original property.
Our experiments show that our technique allows improved handling of
constraint solving instances that are slow to complete on a conventional
solver. Our technique is complementary to existing constraint solving
approaches, in the sense that it can be used to improve the scalability
of any existing tool
Forward Invariant Cuts to Simplify Proofs of Safety
The use of deductive techniques, such as theorem provers, has several
advantages in safety verification of hybrid sys- tems; however,
state-of-the-art theorem provers require ex- tensive manual intervention.
Furthermore, there is often a gap between the type of assistance that a theorem
prover requires to make progress on a proof task and the assis- tance that a
system designer is able to provide. This paper presents an extension to
KeYmaera, a deductive verification tool for differential dynamic logic; the new
technique allows local reasoning using system designer intuition about per-
formance within particular modes as part of a proof task. Our approach allows
the theorem prover to leverage for- ward invariants, discovered using numerical
techniques, as part of a proof of safety. We introduce a new inference rule
into the proof calculus of KeYmaera, the forward invariant cut rule, and we
present a methodology to discover useful forward invariants, which are then
used with the new cut rule to complete verification tasks. We demonstrate how
our new approach can be used to complete verification tasks that lie out of the
reach of existing deductive approaches us- ing several examples, including one
involving an automotive powertrain control system.Comment: Extended version of EMSOFT pape
Faster Constraint Solving Using Learning Based Abstractions
This work addresses the problem of scalable constraint solving. Our
technique combines traditional constraint-solving approaches with
machine learning techniques to propose abstractions that simplify the
problem. First, we use a collection of heuristics to learn sets of constraints
that may be well abstracted as a single, simpler constraint. Next, we
use an asymmetric machine learning procedure to abstract the set of clauses, using
satisfying and falsifying instances as training data. Next, we solve a
reduced constraint problem to check that the learned formula is indeed a
consequent (or antecedent) of the formula we sought to abstract, and
finally we use the learned formula to check the original property.
Our experiments show that our technique allows improved handling of
constraint solving instances that are slow to complete on a conventional
solver. Our technique is complementary to existing constraint solving
approaches, in the sense that it can be used to improve the scalability
of any existing tool
Towards a Learner-Centered Explainable AI: Lessons from the learning sciences
In this short paper, we argue for a refocusing of XAI around human learning
goals. Drawing upon approaches and theories from the learning sciences, we
propose a framework for the learner-centered design and evaluation of XAI
systems. We illustrate our framework through an ongoing case study in the
context of AI-augmented social work.Comment: 7 pages, 2 figure
Indoor robot gardening: design and implementation
This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.Swiss National Science Foundation (contract number PBEL2118737)United States. Army Research Office. Multidisciplinary University Research Initiative (MURI SWARMS project W911NF-05-1-0219)National Science Foundation (U.S.) (NSF IIS-0426838)Intel Corporation (EFRI 0735953 Intel)Massachusetts Institute of Technology (UROP program)Massachusetts Institute of Technology (MSRP program
Classification of driving behaviors using STL formulas: A Comparative Study
In this paper, we conduct a preliminary comparative study of the classification of longitudinal driving behavior using Signal Temporal Logic (STL) formulas. The goal of the classification problem is to distinguish between different driving styles or vehicles. The results can be used to design and test autonomous vehicle policies. We work on a real-life dataset, the Highway Drone Dataset (HighD). To solve this problem, our first approach starts with a formula template and reduces the classification problem to a Mixed-Integer Linear Program (MILP). Solving MILPs becomes computationally challenging with an increasing number of variables and constraints. We propose two improvements to split the classification problem into smaller ones. We prove that these simpler problems are related to the original classification problem in a way that their feasibility implies that of the original. Finally, we compare our MILP formulation with an existing STL-based classification tool, LoTuS, in terms of accuracy and execution time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/173041/1/Classification_of_driving_behaviors_using_STL_formulas_.pdfSEL