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Found 18 papers, 2 papers with code
Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Delivery
We propose a generic multi-robot planning mechanism that combines an optimal task planner and an optimal path planner to provide a scalable solution for complex multi-robot planning problems.
Satisfiability Checking of Multi-Variable TPTL with Unilateral Intervals Is PSPACE-Complete
Moreover, even its 1-variable fragment (1-TPTL$^{0,\infty}$) is strictly more expressive than Metric Interval Temporal Logic (MITL) for which satisfiability checking is EXPSPACE complete.
Markov Decision Processes with Time-Varying Geometric Discounting
We take a game-theoretic perspective -- whereby each time step is treated as an independent decision maker with their own (fixed) discount factor -- and we study the subgame perfect equilibrium (SPE) of the resulting game as well as the related algorithmic problems.
Neural Abstraction-Based Controller Synthesis and Deployment
For the selected benchmarks, our approach reduces the memory requirements respectively for the synthesis and deployment by a factor of $1. 31\times 10^5$ and $7. 13\times 10^3$ on average, and up to $7. 54\times 10^5$ and $3. 18\times 10^4$.
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
In our work, we systematically evaluate two models, ChatGPT (based on GPT-3. 5) and GPT-4, and compare their performance with human tutors for a variety of scenarios.
Sequential Principal-Agent Problems with Communication: Efficient Computation and Learning
In this model, the principal and the agent interact in a stochastic environment, and each is privy to observations about the state not available to the other.
Online Reinforcement Learning with Uncertain Episode Lengths
We show that minimizing regret with this new general discounting is equivalent to minimizing regret with uncertain episode lengths.
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
We investigate using LLMs to generate feedback for fixing syntax errors in Python programs, a key scenario in introductory programming.
Data-Driven Abstraction-Based Control Synthesis
The growth bound together with the sampled trajectories are then used to construct the abstraction and synthesise a controller.
Subcubic Certificates for CFL Reachability
We show that, in both scenarios, there exist succinct certificates ($O(n^2)$ in the size of the problem) and these certificates can be checked in subcubic (matrix multiplication) time.
Formal Languages and Automata Theory Computational Complexity Programming Languages
General Decidability Results for Asynchronous Shared-Memory Programs: Higher-Order and Beyond
We take a language-theoretic perspective and show general decidability and undecidability results for asynchronous programs that capture all known results as well as show decidability of new and important classes.
Formal Languages and Automata Theory Programming Languages F.3.1
Lassie: HOL4 Tactics by Example
We present Lassie, a tactic framework for the HOL4 theorem prover that allows individual users to define their own tactic language by example and give frequently used tactics or tactic combinations easier-to-remember names.
Automated Theorem Proving
Programming Languages
Symbolic Control for Stochastic Systems via Finite Parity Games
While characterizing the exact satisfaction probability is open, we show that a lower bound on this probability can be obtained by (I) computing an under-approximation of the qualitative winning region, i. e., states from which the parity condition can be enforced almost surely, and (II) computing the maximal probability of reaching this qualitative winning region.
Joint Inference of Reward Machines and Policies for Reinforcement Learning
The experiments show that learning high-level knowledge in the form of reward machines can lead to fast convergence to optimal policies in RL, while standard RL methods such as q-learning and hierarchical RL methods fail to converge to optimal policies after a substantial number of training steps in many tasks.
Perception-in-the-Loop Adversarial Examples
We run CMA-ES using human participants to provide the fitness function, using the insight that the choice of best candidates in CMA-ES can be naturally modeled as a perception task: pick the top $k$ inputs perceptually closest to a fixed input.
Precise but Natural Specification for Robot Tasks
We present Flipper, a natural language interface for describing high-level task specifications for robots that are compiled into robot actions.
Automatic Synthesis of Geometry Problems for an Intelligent Tutoring System
This paper presents an intelligent tutoring system, GeoTutor, for Euclidean Geometry that is automatically able to synthesize proof problems and their respective solutions given a geometric figure together with a set of properties true of it.
Approximate Counting in SMT and Value Estimation for Probabilistic Programs
#SMT, or model counting for logical theories, is a well-known hard problem that generalizes such tasks as counting the number of satisfying assignments to a Boolean formula and computing the volume of a polytope.
