Vineet Padmanabhan

The cost of social agents

In this paper we follow the BOID (Belief, Obligation, Intention, Desire) architecture to describe agents and agent types in Defeasible Logic. We argue that the introduction of obligations can provide a new reading of the concepts of intention and intentionality. Then we examine the notion of social agent (i.e., an agent where obligations prevail over intentions) and discuss some computational and philosophical issues related to it. We show that the notion of social agent either requires more complex computations or has some philosophical drawbacks.

A Defeasible Logic of Policy-Based Intention

Most of the theories on formalising intention interpret it as a unary modal operator in Kripkean semantics, which gives it a monotonic look. We argue that policy-based intentions exhibit non-monotonic behaviour which could be captured through a non-monotonic system like defeasible logic. To this end we outline a defeasible logic of intention. The proposed technique alleviates most of the problems related to logical omniscience. The proof theory given shows how our approach helps in the maintenance of intention-consistency in agent systems like BDI.

Dialogue games in defeasible logic

In this paper we show how to capture dialogue games in Defeasible Logic. We argue that Defeasible Logic is a natural candidate and general representation formalism to capture dialogue games even with requirements more complex than existing formalisms for this kind of games. We parse the dialogue into defeasible rules with time of the dialogue as time of the rule. As the dialogue evolves we allow an agent to upgrade the strength of unchallenged rules. The proof procedures of [1] are used to determine the winner of a dialogue game.

On Fibring Semantics for BDI Logics

This study examines BDI logics in the context of Gabbays fibring semantics. We show that dovetailing (a special form of fibring) can be adopted as a semantic methodology to combine BDI logics. We develop a set of interaction axioms that can capture static as well as dynamic aspects of the mental states in BDI systems, using Catachs incestual schema Ga,b,c,d. Further we exemplify the constraints required on fibring function to capture the semantics of interactions among modalities. The advantages of having a fibred approach is discussed in the final section.

Virtual user approach for group recommender systems using precedence relations

We describe virtual user strategy and then examine its properties.The experimental results show that virtual strategy achieve better Precision and Recall.We propose incremental algorithms to update virtual user profile.A new measure called monotonicity is introduced to judge the efficiency of a recommender system.Virtual-user profile + Monotonicity yield recommendations having higher accuracy on benchmark datasets. In this paper, we propose a novel virtual user strategy using precedence relations and develop a new scheme for group recommender systems. User profiles are provided in terms of the precedence relations of items as used by group members. A virtual user for a group is constructed by taking transitive precedence of items of all members into consideration. The profile of the virtual user represents the combined profile of the group. There has not been any earlier attempt to define virtual user profile using precedence relations. We show that the proposed framework exhibits many interesting properties. Earlier approaches construct virtual user profile by considering the set of common items used by all members of the group. In the present work, we propose a method of computing weightage for each item, not necessarily common to all members, using transitive precedence. We also introduce a new measure called monotonicity to measure the performance of any recommender system. In a top-k recommendation, monotonicity tries to measure the number of items continued to be recommended when a technique is utilized incrementally. We experimented extensively for different combinations of parameter settings and for different group sizes on MovieLens data. We show that our framework has better performance in terms of precision and recall when compared with other methods. We show that our recommendation framework exhibits robust monotonicity.

Actions Made Explicit in BDI

The Belief, Desire, Intention (BDI) architecture is increasingly being used in a wide range of complex applications for agents. Many theories and models exist which support this architecture and the recent version is that of Capability being added as an additional construct. In all these models the concept of action is seen in an endogenous manner. We argue that the Result of an action performed by an agent is extremely important when dealing with composite actions and hence the need for an explicit representation of them. The Capability factor is supported using a RES construct and it is shown how the components of a composite action is supported using these two. Further, we introduce an OPP (opportunity) operator which in alliance with Result and Capability provides better semantics for practical reasoning in BDI.

Efficient computation for probabilistic skyline over uncertain preferences

We propose an efficient algorithm that can compute skyline probability exactly for reasonably large database.We introduce the concept of zero-contributing set which has zero effect in the signed aggregate of joint probabilities.We propose an incremental algorithm to compute skyline probability in dynamic scenarios wherein objects are added incrementally.The theoretical concepts developed helps to devise an efficient technique to compute skyline probability of all objects in the database.Detailed experimental analysis for real and synthetic datasets are reported to corroborate our findings. Efficient computation of skyline probability over uncertain preferences has not received much attention in the database community as compared to skyline probability computation over uncertain data. All known algorithms for probabilistic skyline computation over uncertain preferences attempt to find inexact value of skyline probability by resorting to sampling or to approximation scheme. Exact computation of skyline probability for database with uncertain preferences of moderate size is not possible with any of the existing algorithms. In this paper, we propose an efficient algorithm that can compute skyline probability exactly for reasonably large database. The inclusion-exclusion principle is used to express skyline probability in terms of joint probabilities of all possible combination. In this regard we introduce the concept of zero-contributing set which has zero effect in the signed aggregate of joint probabilities. Our algorithm employs a prefix-based k-level absorption to identify zero-contributing sets. It is shown empirically that only a very small portion of exponential search space remains after level wise application of prefix-based absorption. Thus it becomes possible to compute skyline probability with respect to large datasets. Detailed experimental analysis for real and synthetic datasets are reported to corroborate this claim. We also propose an incremental algorithm to compute skyline probability in dynamic scenarios wherein objects are added incrementally. Moreover, the theoretical concepts developed in this paper help to devise an efficient technique to compute skyline probability of all objects in the database. We show that the exponential search space is pruned once and then for each individual object skyline probability can be derived by inspecting a portion of the pruned lattice. We also use a concept of revival of absorbed pairs. We believe that this process is more efficient than computing the skyline probability individually.

Collaborative filtering using multiple binary maximum margin matrix factorizations

In MMMF, ratings matrix with multiple discrete values is treated by specially extending hinge loss function to suit multiple levels.We view this process as analogous to extending two-class classifier to a unified multi-class classifier.Alternatively, multi-class classifier can be built by arranging multiple two- class classifiers in a hierarchical manner.In this paper, we investigate this aspect for collaborative filtering and propose an efficient and novel framework of multiple bi-level MMMFs.We compare our method with nine well-known algorithms on two benchmark datasets and show that our method outperforms these methods on NMAE measure. Maximum Margin Matrix Factorization (MMMF) has been a successful learning method in collaborative filtering research. For a partially observed ordinal rating matrix, the focus is on determining low-norm latent factor matrices U (of users) and V (of items) so as to simultaneously approximate the observed entries under some loss measure and predict the unobserved entries. When the rating matrix contains only two levels (1), rows of V can be viewed as points in k-dimensional space and rows of U as decision hyperplanes in this space separating +1 entries from 1 entries. When hinge/smooth hinge loss is the loss function, the hyperplanes act as maximum-margin separator. In MMMF, rating matrix with multiple discrete values is treated by specially extending hinge loss function to suit multiple levels. We view this process as analogous to extending two-class classifier to a unified multi-class classifier. Alternatively, multi-class classifier can be built by arranging multiple two-class classifiers in a hierarchical manner. In this paper, we investigate this aspect for collaborative filtering and propose an efficient and novel framework of multiple bi-level MMMFs. There is substantial saving in computational overhead. We compare our method with nine well-known algorithms on two benchmark datasets and show that our method outperforms these methods on NMAE measure. We also show that our method yields latent factors of lower ranks and the trade-off between empirical and generalization error is low.

Collaborative filtering by PSO-based MMMF

Matrix factorization (MF) techniques are one of the most succesful realisations of recommender systems based on collaborative filtering/prediction (CF). For instance, in a movie recommender system based on CF, the inputs to the system are user ratings on movies (items) the users have already seen. To predict user preferences on movies they have not yet watched one needs to understand the patterns in the partially observed rating matrix. It is possible to visualize this setting as a matrix completion problem, i.e., completing entries in a partially observed data matrix. Then the objective is to compute user latent factor and item latent factor such that the rating matrix is completed. The factorization is usually accomplished by minimizing an objective function using gradient descent or its variants such as conjugate gradient or stochastic gradient descent. In this paper we make use of a particular MF technique called Maximum Margin Matrix Factorization (MMMF) and show that it is suitable for multi-level discrete rating matrix. The factorization is accomplished by minimizing the hinge loss objective function. We propose to improve the gradient search by combining a component of particle Swarm Optimisation (PSO) search. Though earlier attempts of improving PSO search by adding gradient information exist, the main objective of the present work is to improvise gradient/stochastic-gradient search. Our proposed algorithm finds better minimizing points early (fewer number of iterations) not only for the loss function but also for other performance metrics of collaborative filtering such as RMSE and MAE. There has not been any earlier attempt to combine particle swarm optimisation with maximum margin matrix factorisation for collaborative filtering.

An Asymmetric Protocol for Argumentation Games in Defeasible Logic

Agent interactions where the agents hold conflicting goals could be modelled as adversarial argumentation games. In many real-life situations (e.g., criminal litigation, consumer legislation), due to ethical, moral or other principles governing interaction, the burden of proof, i.e., which party is to lose if the evidence is balanced [22], is a priori fixed to one of the parties. Analogously, when resolving disputes in a heterogeneous agent-system the unequal importance of different agents for carrying out the overall system goal need to be accounted for. In this paper we present an asymmetric protocol for an adversarial argumentation game in Defeasible Logic, suggesting Defeasible Logic as a general representation formalism for argumentation games modelling agent interactions.