19 research outputs found
Neural-Symbolic Recommendation with Graph-Enhanced Information
The recommendation system is not only a problem of inductive statistics from
data but also a cognitive task that requires reasoning ability. The most
advanced graph neural networks have been widely used in recommendation systems
because they can capture implicit structured information from graph-structured
data. However, like most neural network algorithms, they only learn matching
patterns from a perception perspective. Some researchers use user behavior for
logic reasoning to achieve recommendation prediction from the perspective of
cognitive reasoning, but this kind of reasoning is a local one and ignores
implicit information on a global scale. In this work, we combine the advantages
of graph neural networks and propositional logic operations to construct a
neuro-symbolic recommendation model with both global implicit reasoning ability
and local explicit logic reasoning ability. We first build an item-item graph
based on the principle of adjacent interaction and use graph neural networks to
capture implicit information in global data. Then we transform user behavior
into propositional logic expressions to achieve recommendations from the
perspective of cognitive reasoning. Extensive experiments on five public
datasets show that our proposed model outperforms several state-of-the-art
methods, source code is avaliable at [https://github.com/hanzo2020/GNNLR].Comment: 12 pages, 2 figures, conferenc
Neuro-Symbolic Recommendation Model based on Logic Query
A recommendation system assists users in finding items that are relevant to
them. Existing recommendation models are primarily based on predicting
relationships between users and items and use complex matching models or
incorporate extensive external information to capture association patterns in
data. However, recommendation is not only a problem of inductive statistics
using data; it is also a cognitive task of reasoning decisions based on
knowledge extracted from information. Hence, a logic system could naturally be
incorporated for the reasoning in a recommendation task. However, although
hard-rule approaches based on logic systems can provide powerful reasoning
ability, they struggle to cope with inconsistent and incomplete knowledge in
real-world tasks, especially for complex tasks such as recommendation.
Therefore, in this paper, we propose a neuro-symbolic recommendation model,
which transforms the user history interactions into a logic expression and then
transforms the recommendation prediction into a query task based on this logic
expression. The logic expressions are then computed based on the modular logic
operations of the neural network. We also construct an implicit logic encoder
to reasonably reduce the complexity of the logic computation. Finally, a user's
interest items can be queried in the vector space based on the computation
results. Experiments on three well-known datasets verified that our method
performs better compared to state of the art shallow, deep, session, and
reasoning models.Comment: 17 pages, 6 figure
A sequential model of bargaining in logic programming
This paper proposes a sequential model of bargaining specifying reasoning processes of an agent behind bargaining procedures. We encode agents’ background knowledge, demands, and bargaining constraints in logic programs and represent bargaining outcomes in answer sets. We assume that in each bargaining situation, each agent has a set of goals to achieve, which are normally unachievable without an agreement among all the agents who are involved in the bargaining. Through an alternating-offers procedure, an agreement among bargaining agents may be reached by abductive reasoning.We show that the procedure converges to a Nash equilibrium if each agent makes rational offers/counter-offers in each round. In addition, the sequential model also has a number of desirable properties, such as mutual commitments, individual rationality, satisfactoriness, and honesty
A sequential model for reasoning about bargaining in logic programs
This paper presents a sequential model of bargaining based on abductive reasoning in ASP. We assume that each agent is represented by a logic program that encodes the background knowledge of the agent. Each agent has a set of goals to achieve but these goals are normally unachievable without an agreement from the other agent. We design an alternating-offers procedure that shows how an agreement between two agents can be reached through a reasoning process based on answer set programming and abduction. We prove that the procedure converges to a Nash equilibrium if each player makes rational offer/counter-offer at each round
Language splitting and relevance-based belief change in Horn logic
This paper presents a framework for relevance-based belief change in propositional Horn logic.We firstly establish a parallel interpolation theorem for Horn logic and show that Parikh’s Finest Splitting Theorem holds with Horn formulae. By reformulating Parikh’s relevance criterion in the setting of Horn belief change, we construct a relevance-based partial meet Horn contraction operator and provide a representation theorem for the operator. Interestingly, we find that this contraction operator can be fully characterised by Delgrande and Wassermann’s postulates for partial meet Horn contraction as well as Parikh’s relevance postulate without requiring any change on the postulates, which is qualitatively different from the case in classical propositional logic
Indoor Mobile Robot Positioning Based on Wireless Fingerprint Matching
This paper discusses the design of an indoor mobile robot positioning system. The problem of indoor positioning is solved through Wi-Fi fingerprint positioning to implement a low cost deployment. A wireless fingerprint matching algorithm based on the similarity of unequal length sequences is presented. Candidate sequences selection is defined as a set of mappings, and detection errors caused by wireless hotspot stability and the change of interior pattern can be corrected by transforming the unequal length sequences into equal length sequences. The presented scheme was verified experimentally to achieve the accuracy requirements for an indoor positioning system with low deployment cost
Prediction of spherical equivalent refraction and axial length in children based on machine learning
Purpose: Recently, the proportion of patients with high myopia has shown a continuous growing trend, more toward the younger age groups. This study aimed to predict the changes in spherical equivalent refraction (SER) and axial length (AL) in children using machine learning methods. Methods: This study is a retrospective study. The cooperative ophthalmology hospital of this study collected data on 179 sets of childhood myopia examinations. The data collected included AL and SER from grades 1 to 6. This study used the six machine learning models to predict AL and SER based on the data. Six evaluation indicators were used to evaluate the prediction results of the models. Results: For predicting SER in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the multilayer perceptron (MLP) algorithm, MLP algorithm, orthogonal matching pursuit (OMP) algorithm, OMP algorithm, and OMP algorithm, respectively. The R2 of the five models were 0.8997, 0.7839, 0.7177, 0.5118, and 0.1758, respectively. For predicting AL in grade 6, grade 5, grade 4, grade 3, and grade 2, the best results were obtained through the Extra Tree (ET) algorithm, MLP algorithm, kernel ridge (KR) algorithm, KR algorithm, and MLP algorithm, respectively. The R2 of the five models were 0.7546, 0.5456, 0.8755, 0.9072, and 0.8534, respectively. Conclusion: Therefore, in predicting SER, the OMP model performed better than the other models in most experiments. In predicting AL, the KR and MLP models were better than the other models in most experiments