Next Point-Of-Interest (POI) recommendation plays an important role in various location-based services.Its main objective is to predict the users' next interested POI based on their previous check-in information.Most existing studies view the next POI recommendation as a sequence prediction problem but pay little attention to the fine-grained latent preferences of users, neglecting the diversity of user motivations on visiting the POIs.In this paper, we propose a contrastive learning framework based on latent preferences (CLLP) for next POI recommendation, which models the latent preference distributions of users at each POI and then yield disentangled latent preference representations.Specifically, we leverage the cross-local and global spatio-temporal contexts to learn POI representations for dynamically modeling user preferences.And we design a novel distillation strategy to make full use of the collaborative signals from other users for representation optimization.Then, we disentangle multiple latent preferences in POI representations using predefined preference prototypes, while leveraging preference-level contrastive learning to encourage independence of different latent preferences by improving the quality of latent preference representation space.Meanwhile, we employ a multi-task training strategy to jointly optimize all parameters.Experimental results on two real-world datasets show that CLLP achieves the state-of-the-art performance and significantly outperforms all existing solutions.Further investigations demonstrate the robustness of CLLP against sparse and noisy data.

The technique called splitting sets has been proven useful in simplifying the investigation of Answer Set Programming (ASP). In this paper, we investigate the splitting set theorem for LPMLN that is a new extension of ASP created by combining the ideas of ASP and Markov Logic Networks (MLN). Firstly, we extend the notion of splitting sets to LPMLN programs and present the splitting set theorem for LPMLN. Then, the use of the theorem for simplifying several LPMLN inference tasks is illustrated. After that, we give two parallel approaches for solving LPMLN programs via using the theorem. The preliminary experimental results show that these approaches are alternative ways to promote an LPMLN solver.

LPMLNextends the language of Answer Set Programming (ASP) by assigning a weight degree to each rule so that its stable models do not have to satisfy all LPMLNrules, which is rooted in the manner of Markov Logic Networks (MLN) to handle the uncertainties and inconsistencies in knowledge representation and reasoning. Due to its expressibility, LPMLNhas been employed in several real world applications. However, an LPMLNprogram is much harder to solve than its unweighted counterpart (an ASP program), and only some preliminary solvers have been implemented so far, which is preventing further studies in both theoretical and practical sides. There are three main contributions in this paper. Firstly, we present an LPMLNsolver: LPMLNModels, which is able to run concurrently. Secondly, we present parallel methods in LPMLNModels. For splitting set method, we present an algorithm to generate a proper splitting set, which is an essential part of the method. For augmented subset method, we present a heuristic method to improve its performance. Finally, we present hybrid methods in LPMLNModels to better utilize the parallel methods. The experimental results show that our algorithms and improvements in this paper works and hybrid methods have better performance in general.

This paper develops a probabilistic-epistemic logic program language, PELP, by introducing probabilistic modal operators Kwand PL into LPMLNprograms, where w is a sub-interval of [0, 1]. Intuitively, a probabilistic epistemic literal Kwe denotes that e is known with a probability in w, and a probabilistic comparing literal PL(e1, e2) denotes it is known that the probability of e1is less than the one of e2. The semantics of the new language is based on the semantics of LPMLNand epistemic specifications. In this paper, we analyze the relationship between PELP and some other epistemic logic programming languages. We also propose an algorithm for solving PELP programs, and then investigate the application of PELP for modeling and solving the Monty Hall problem and a conformant planning problem with a threshold.

This paper develops a logic programming language, GI-log, that extends answer set programming language with a new graded modality Kω where ω is an interval satisfying ω ⊆ [0, 1]. The modality is used to precede a literal in rules bodies, and thus allows for the representation of graded introspectio ns in the presence of multiple belief sets: KωF intuitively means: it is known that the proportion of the belief sets where F is true is in the interval ω. We define the semantics of GI-log, study the relation to the languages of strong introspections, give an algorithm for computing solutions of GI-log programs, and investigate the use of GI-log for formalizing contextual reasoning, conformant planning with threshold, and modeling a graph problem.

Epistemic specification (ES for short) is an extension of answer set programming (ASP for short). The extension is built around the introduction of modalities K and M, and then is capable of representing incomplete information in the presence of multiple belief sets. Although both syntax and semantics of ES are up in the air, the need for this extension has been illustrated with several examples in the literatures. In this paper, we present a new ES version with only modality K and the design of its inference engine ESmodels that aims to be efficient enough to promote the theoretical research and also practical use of ES. We first introduce the syntax and semantics of the new version of ES and show it is succinct but flexible by comparing it with existing ES versions. Then, we focus on the description of the algorithm and optimization approaches of the inference engine. Finally, we conclude with perspectives.

Medical treatment decision making is a good application of knowledge representation and reasoning. We are particularly interested in using it to resolve treatment conflicts, a complicated condition when two treatments cannot be given simultaneously to a patient of multiple symptoms. The logic system is required to reason on cases with and without treatment conflicts. Thanks to the nonmonotonicity of Answer Set Programming (ASP), we elegantly automate medical treatment conflict resolving on an example problem and show the importance of nonmonotonicity in medical reasoning.

In this paper we extend RPBL, a Relational Paths Based Learning approach for first order theories in three directions. We apply domain theories to expand structured instance space, learn recursive theories by an example of learningmember relationship of lists, and analyze the performance as well as time complexity theoretically. In addition, we give the details of our experimental results.