Constraint-Based Reasoning

SafeAI 2019 – AAAI's Workshop on Artificial Intelligence Safety


You are invited to submit short position papers (2-4 pages), full technical papers (6-8 pages) or proposals for technical talks (one-page abstract). The workshop proceedings will be published on SafeAI will be a memorable event with two top keynote speakers and three great invited talks. The Program is now available! The DARPA Assured Autonomy program aims to advance the ways computing systems can learn and evolve with machine learning to better manage variations in the environment and enhance the predictability of autonomous systems like driverless vehicles.

How to start learning Artificial Intelligence & Machine Learning


Below you'll see a rundown of Artificial Intelligence Resources to Learn, how to start in Artificial Intelligence in Easy steps: This course gives the basics of Artificial Intelligence (AI), and apply them. Object intelligent agents to resolve real world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

Learning Constraints from Demonstrations Artificial Intelligence

We extend the learning from demonstration paradigm by providing a method for learning unknown constraints shared across tasks, using demonstrations of the tasks, their cost functions, and knowledge of the system dynamics and control constraints. Given safe demonstrations, our method uses hit-and-run sampling to obtain lower cost, and thus unsafe, trajectories. Both safe and unsafe trajectories are used to obtain a consistent representation of the unsafe set via solving an integer program. Our method generalizes across system dynamics and learns a guaranteed subset of the constraint. We also provide theoretical analysis on what subset of the constraint can be learnable from safe demonstrations. We demonstrate our method on linear and nonlinear system dynamics, show that it can be modified to work with suboptimal demonstrations, and that it can also be used to learn constraints in a feature space.

Consistency for 0-1 Programming Artificial Intelligence

Concepts of consistency have long played a key role in constraint programming but never developed in integer programming (IP). Consistency nonetheless plays a role in IP as well. For example, cutting planes can reduce backtracking by achieving various forms of consistency as well as by tightening the linear programming (LP) relaxation. We introduce a type of consistency that is particularly suited for 0-1 programming and develop the associated theory. We define a 0-1 constraint set as LP-consistent when any partial assignment that is consistent with its linear programming relaxation is consistent with the original 0-1 constraint set. We prove basic properties of LP-consistency, including its relationship with Chvatal-Gomory cuts and the integer hull. We show that a weak form of LP-consistency can reduce or eliminate backtracking in a way analogous to k-consistency but is easier to achieve. In so doing, we identify a class of valid inequalities that can be more effective than traditional cutting planes at cutting off infeasible 0-1 partial assignments.

Selected Qualitative Spatio-temporal Calculi Developed for Constraint Reasoning: A Review Artificial Intelligence

In this article a few of the qualitative spatio-temporal knowledge representation techniques developed by the constraint reasoning community within artificial intelligence are reviewed. The objective is to provide a broad exposure to any other interested group who may utilize these representations. The author has a particular interest in applying these calculi (in a broad sense) in topological data analysis, as these schemes are highly qualitative in nature.

Streamlining Variational Inference for Constraint Satisfaction Problems Artificial Intelligence

Several algorithms for solving constraint satisfaction problems are based on survey propagation, a variational inference scheme used to obtain approximate marginal probability estimates for variable assignments. These marginals correspond to how frequently each variable is set to true among satisfying assignments, and are used to inform branching decisions during search; however, marginal estimates obtained via survey propagation are approximate and can be self-contradictory. We introduce a more general branching strategy based on streamlining constraints, which sidestep hard assignments to variables. We show that streamlined solvers consistently outperform decimation-based solvers on random k-SAT instances for several problem sizes, shrinking the gap between empirical performance and theoretical limits of satisfiability by 16.3% on average for k 3, 4, 5, 6.

Bandits with Temporal Stochastic Constraints Artificial Intelligence

We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for an action if they have played it at least a few times in the recent past. It is practically motivated by repetition and recency effects in domains such as advertising (here consumer behavior may require repeat actions by advertisers) and vocational training (here actions are complex skills that can only be mastered with repetition to get a payoff). Impairment can be naturally modelled as a temporal constraint on the strategy space, and we provide two novel algorithms that achieve sublinear regret, each working with different assumptions on the impairment effect. We introduce a new notion called bucketing in our algorithm design, and show how it can effectively address impairment as well as a broader class of temporal constraints. Our regret bounds explicitly capture the cost of impairment and show that it scales (sub-)linearly with the degree of impairment. Our work complements recent work on modeling delays and corruptions, and we provide experimental evidence supporting our claims.

Constraint-based Sequential Pattern Mining with Decision Diagrams Artificial Intelligence

Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.

Stratified Constructive Disjunction and Negation in Constraint Programming Artificial Intelligence

Constraint Programming (CP) is a powerful declarative programming paradigm combining inference and search in order to find solutions to various type of constraint systems. Dealing with highly disjunctive constraint systems is notoriously difficult in CP. Apart from trying to solve each disjunct independently from each other, there is little hope and effort to succeed in constructing intermediate results combining the knowledge originating from several disjuncts. In this paper, we propose If Then Else (ITE), a lightweight approach for implementing stratified constructive disjunction and negation on top of an existing CP solver, namely SICStus Prolog clp(FD). Although constructive disjunction is known for more than three decades, it does not have straightforward implementations in most CP solvers. ITE is a freely available library proposing stratified and constructive reasoning for various operators, including disjunction and negation, implication and conditional. Our preliminary experimental results show that ITE is competitive with existing approaches that handle disjunctive constraint systems.

CatSAT: A Practical, Embedded, SAT Language for Runtime PCG

AAAI Conferences

Answer-set programming (ASP), a family of SAT-based logic programming systems, is attractive for procedural content generation. Unfortunately, current solvers present significant barriers to runtime use in games. In this paper, I discuss some of the issues involved, and present CatSAT, a solver designed to better fit the run-time resource constraints of modern games. Although intended only for small problems, it allows designers to compactly specify simple PCG problems such as NPC generation, solve them in a few tens of microseconds, and to adapt solutions dynamically based on the changing needs of gameplay. We hope that by making adoption as convenient as possible, we can increase the uptake of declarative techniques among developers.