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Register a new userThe Temporal Dynamics of Belief-based Updating of Epistemic Trust: Light at the End of the Tunnel?
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Momme Von Sydow
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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We start with the distinction of outcome- and belief-based Bayesian models of the sequential update of agents beliefs and subjective reliability of sources (trust). We then focus on discussing the influential Bayesian model of belief-based trust update by Eric Olsson, which models dichotomic events and explicitly represents anti-reliability. After sketching some disastrous recent results for this perhaps most promising model of belief update, we show new simulation results for the temporal dynamics of learning belief with and without trust update and with and without communication. The results seem to shed at least a somewhat more positive light on the communicating-and-trust-updating agents. This may be a light at the end of the tunnel of belief-based models of trust updating, but the interpretation of the clear findings is much less clear.
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In recent years, multi-agent epistemic planning has received attention from both dynamic logic and planning communities. Existing implementations of multi-agent epistemic planning are based on compilation into classical planning and suffer from various limitations, such as generating only linear plans, restriction to public actions, and incapability to handle disjunctive beliefs. In this paper, we propose a general representation language for multi-agent epistemic planning where the initial KB and the goal, the preconditions and effects of actions can be arbitrary multi-agent epistemic formulas, and the solution is an action tree branching on sensing results. To support efficient reasoning in the multi-agent KD45 logic, we make use of a normal form called alternating cover disjunctive formulas (ACDFs). We propose basic revision and update algorithms for ACDFs. We also handle static propositional common knowledge, which we call constraints. Based on our reasoning, revision and update algorithms, adapting the PrAO algorithm for contingent planning from the literature, we implemented a multi-agent epistemic planner called MEPK. Our experimental results show the viability of our approach.
This paper presents a two-dimensional modal logic for reasoning about the changing patterns of knowledge and social relationships in networks organised on the basis of a symmetric friendship relation, providing a precise language for exploring logic in the community [11]. Agents are placed in the model, allowing us to express such indexical facts as I am your friend and You, my friends, are in danger. The technical framework for this work is general dynamic dynamic logic (GDDL) [4], which provides a general method for extending modal logics with dynamic operators for reasoning about a wide range of model-transformations, starting with those definable in propositional dynamic logic (PDL) and extended to allow for the more subtle operators involved in, for example, private communication, as represented in dynamic epistemic logic (DEL) and related systems. We provide a hands-on introduction to GDDL, introducing elements of the formalism as we go, but leave the reader to consult [4] for technical details. Instead, the purpose of this paper is to investigate a number of conceptual issues that arise when considering communication between agents in such networks, both from one agent to another, and broadcasts to socially-defined groups of agents, such as the group of my friends.
We build simple computational models of belief dynamics within the framework of discrete-spin statistical physics models, and explore how suitable they are for understanding and predicting real-world belief change on both the individual and group levels. We find that accurate modeling of real-world patterns requires attending to social interaction rules that people use, network structures in which they are embedded, distributions of initial beliefs and intrinsic preferences, and the relative importance of social information and intrinsic preferences. We demonstrate that these model parameters can be constrained by empirical measurement, and the resulting models can be used to investigate the mechanisms underlying belief dynamics in actual societies. We use data from two longitudinal studies of belief change, one on 80~individuals living in an MIT dorm during the 2008 presidential election season, and another on 94~participants recruited from Mechanical Turk during the 2016 presidential election primary season. We find that simple statistical physics-based models contain predictive value for real-world belief dynamics and enable empirical tests of different assumptions about the underlying network structure and the social interaction rules.
In this work, we introduce a new approach for the efficient solution of autonomous decision and planning problems, with a special focus on decision making under uncertainty and belief space planning (BSP) in high-dimensional state spaces. Usually, to solve the decision problem, we identify the optimal action, according to some objective function. We claim that we can sometimes generate and solve an analogous yet simplified decision problem, which can be solved more efficiently; a wise simplification method can lead to the same action selection, or one for which the maximal loss can be guaranteed. Furthermore, such simplification is separated from the state inference, and does not compromise its accuracy, as the selected action would finally be applied on the original state. First, we present the concept for general decision problems, and provide a theoretical framework for a coherent formulation of the approach. We then practically apply these ideas to BSP problems, which can be simplified by considering a sparse approximation of the initial (Gaussian) belief. The scalable belief sparsification algorithm we provide is able to yield solutions which are guaranteed to be consistent with the original problem. We demonstrate the benefits of the approach in the solution of a highly realistic active-SLAM problem, and manage to significantly reduce computation time, with practically no loss in the quality of solution. This work is conceptual and fundamental, and holds numerous possible extensions.
Belief revision is an operation that aims at modifying old be-liefs so that they become consistent with new ones. The issue of belief revision has been studied in various formalisms, in particular, in qualitative algebras (QAs) in which the result is a disjunction of belief bases that is not necessarily repre-sentable in a QA. This motivates the study of belief revision in formalisms extending QAs, namely, their propositional clo-sures: in such a closure, the result of belief revision belongs to the formalism. Moreover, this makes it possible to define a contraction operator thanks to the Harper identity. Belief revision in the propositional closure of QAs is studied, an al-gorithm for a family of revision operators is designed, and an open-source implementation is made freely available on the web.
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