A Qualitative Constraint Network (QCN) is a constraint graph for representing problems under qualitative temporal and spatial relations, among others. More formally, a QCN includes a set of entities, and a list of qualitative constraints defining the possible scenarios between these entities. These latter constraints are expressed as disjunctions of binary relations capturing the (incomplete) knowledge between the involved entities. QCNs are very effective in representing a wide variety of real-world applications, including scheduling and planning, configuration and Geographic Information Systems (GIS). It is however challenging to elicit, from the user, the QCN representing a given problem. To overcome this difficulty in practice, we propose a new algorithm for learning, through membership queries, a QCN from a non expert. In this paper, membership queries are asked in order to elicit temporal or spatial relationships between pairs of temporal or spatial entities. In order to improve the time performance of our learning algorithm in practice, constraint propagation, through transitive closure, as well as ordering heuristics, are enforced. The goal here is to reduce the number of membership queries needed to reach the target QCN. In order to assess the practical effect of constraint propagation and ordering heuristics, we conducted several experiments on randomly generated temporal and spatial constraint network instances. The results of the experiments are very encouraging and promising.

Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency? These scenarios force us to evaluate the trade-off between collective norms and our own personal objectives. To create effective AI-human teams, we must equip AI agents with a model of how humans make trade-offs in complex, constrained environments. These agents will be able to mirror human behavior or to draw human attention to situations where decision making could be improved. To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings. In addition, learning soft constraints over states, actions, and state features allows agents to transfer this knowledge to new domains that share similar aspects. We then use the constraint learning method to implement a novel system architecture that leverages a cognitive model of human decision making, multi-alternative decision field theory (MDFT), to orchestrate competing objectives. We evaluate the resulting agent on trajectory length, number of violated constraints, and total reward, demonstrating that our agent architecture is both general and achieves strong performance. Thus we are able to capture and replicate human-like trade-offs from demonstrations in environments when constraints are not explicit.

Artificial Intelligence/Machine Learning field is getting a lot of attention right now, and knowing where to start can be a little difficult. I've been dabbling in this field, so I thought of curating the best resources in one place. All of these are curated based on if it's an inspiring read or a valuable resource. I hope this curated list help you get started on what you need to know about AI/Machine Learning on a technical level. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.

The expressiveness of propositional non-clausal (NC) formulas is exponentially richer than that of clausal formulas. Yet, clausal efficiency outperforms non-clausal one. Indeed, a major weakness of the latter is that, while Horn clausal formulas, along with Horn algorithms, are crucial for the high efficiency of clausal reasoning, no Horn-like formulas in non-clausal form had been proposed. To overcome such weakness, we define the hybrid class $\mathbb{H_{NC}}$ of Horn Non-Clausal (Horn-NC) formulas, by adequately lifting the Horn pattern to NC form, and argue that $\mathbb{H_{NC}}$, along with future Horn-NC algorithms, shall increase non-clausal efficiency just as the Horn class has increased clausal efficiency. Secondly, we: (i) give the compact, inductive definition of $\mathbb{H_{NC}}$; (ii) prove that syntactically $\mathbb{H_{NC}}$ subsumes the Horn class but semantically both classes are equivalent, and (iii) characterize the non-clausal formulas belonging to $\mathbb{H_{NC}}$. Thirdly, we define the Non-Clausal Unit-Resolution calculus, $UR_{NC}$, and prove that it checks the satisfiability of $\mathbb{H_{NC}}$ in polynomial time. This fact, to our knowledge, makes $\mathbb{H_{NC}}$ the first characterized polynomial class in NC reasoning. Finally, we prove that $\mathbb{H_{NC}}$ is linearly recognizable, and also that it is both strictly succincter and exponentially richer than the Horn class. We discuss that in NC automated reasoning, e.g. satisfiability solving, theorem proving, logic programming, etc., can directly benefit from $\mathbb{H_{NC}}$ and $UR_{NC}$ and that, as a by-product of its proved properties, $\mathbb{H_{NC}}$ arises as a new alternative to analyze Horn functions and implication systems.

In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses. Regrasping is needed whenever a robot's current grasp pose fails to perform desired manipulation tasks. Endowing robots with such an ability has applications in many domains such as manufacturing or domestic services. Yet, it is a challenging task due to the large diversity of geometry in everyday objects and the high dimensionality of the state and action space. In this paper, we propose a system for robots to take partial point clouds of an object and the supporting environment as inputs and output a sequence of pick-and-place operations to transform an initial object grasp pose to the desired object grasp poses. The key technique includes a neural stable placement predictor and a regrasp graph based solution through leveraging and changing the surrounding environment. We introduce a new and challenging synthetic dataset for learning and evaluating the proposed approach. In this dataset, we show that our system is able to achieve 73.3% success rate of regrasping diverse objects.

This is a preliminary work on configuration knowledge representation which serves as a foundation for building interactive configuration systems in Answer Set programming (ASP). The major concepts of the product configuration problem are identified and discussed with a bike configuration example. A fact format is developed for expressing product knowledge that is domain-specific and can be mapped from other systems. Finally, a domain-independent ASP encoding is provided that represents the concepts in the configuration problem.

Picat [27] is a Prolog-like language that takes many features from other languages, including patternmatching rules, functions, list/array comprehensions, loops, assignments, tabling for dynamic programming and planning, and constraint programming. These features make Picat a convenient modeling language for combinatorial problems, on a par with AMPL [8], OPL [9], and MiniZinc [17]. As a logic language, Picat can often offer solutions that are as concise and elegant as the ones in ASP [5]. Picat supports constraint solving using different solvers, including CP (constraint programming), SAT (satisfiability), MIP (mixed integer programming), and SMT (SAT Modulo Theories). The last two decades have witnessed dramatic enhancement in SAT solvers' performance, thanks to inventions of techniques, from conflict-driven clause learning, backjumping, variable and value selection heuristics, to random restarts [2, 4, 16]. With findings of effective encodings [12, 13, 15, 19, 21, 23, 26], SAT has become a strong contendant for solving a wide range of constraint satisfaction and optimization problems (CSP). Many CSPs involve synthesizing subgraphs that satisfy certain reachability constraints, including the constraint that ensures a cycle connecting all the vertices, as in the Hamiltonian cycle problem (HCP), and the constraint that ensures a strongly connected component. For that reason, CP systems provide graph constraints for easing the modeling and solving of these problems [1, 20].

Discovering relevant patterns for a particular user remains a challenging task in data mining. In real-life applications, relevant patterns may be either frequent or rare ones in the data. In itemset mining, setting the minimum support threshold is a real dilemma (a high value misses rare itemsets, a low value generates a large number of meaningless itemsets). To tackle the rare item problem [8], several approaches were proposed to mine frequent pattern with multiple minimum supports. In [8], the problem of mining frequent itemsets with multiple Minimum Item Supports (MIS) was introduced with a first revision of Apriori algorithm (MSApriori). Then, other Apriori-like approaches were proposed like MMS Cumulate and MMS Stratify [12]. The well-known FPGrowth was extended with a condensed FP-tree structure to mine frequent itemsets with multiple MIS (CFPGrowth [5], CFPGrowth [6]). In [3], FP ME was proposed based on set-enumeration-tree structure and sorted downward closure property. The specialized algorithms introduced previously are effective for mining patterns with multiple MIS.

We developed and compared Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock optimisation considering necessary maintenance tasks. To deal with such problems in CP we investigated specialised pruning rules and implemented them in a global constraint. For the QA approach, we developed quadratic unconstrained binary optimisation (QUBO) models. For testing, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to run the CP approach as well as tabu search for the QUBO models. We find that both approaches tend at the current development stage of the physical quantum annealers to produce comparable results, with the caveat that QUBO does not always guarantee that the maintenance constraints hold, which we fix by adjusting the QUBO model in preprocessing, based on how close the trains are to a maintenance threshold distance.