In this work, we formulate the process of generating explanations as model reconciliation for planning problems as one of planning with explanatory actions. We show that these problems could be better understood within the framework of epistemic planning and that, in fact, most earlier works on explanation as model reconciliation correspond to tractable subsets of epistemic planning problems. We empirically show how our approach is computationally more efficient than existing techniques for explanation generation and we end the paper with a discussion of how this formulation could be extended to generate novel explanatory behaviors.
Our work also looks at the use of explanatory actions as a means of communicating information to the human observer. The most obvious types of such explanatory action includes purely communicative actions such as speech @cite_5 or the use of mixed reality projections @cite_18 @cite_4 , but recent works have shown that physical agents could also use movements to relay information such as intention @cite_6 @cite_17 and incapability @cite_22 . Our framework could be easily adopted to any of these explanatory actions and would naturally allow for a trade-off between these different types of communication.
@cite_18 @cite_4 @cite_22 @cite_6 @cite_5 @cite_17
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Abstract Knowledge-based systems that interact with humans often need to define their terminology, elucidate their behavior or support their recommendations or conclusions. In general, they need to explain themselves. Unfortunately, current computer systems, if they can explain themselves at all, often generate explanations that are unnatural, ill-connected or simply incoherent. They typically have only one method of explanation which does not allow them to recover from failed communication. At a minimum, this can irritate an end-user and potentially decrease their productivity. More dangerous, poorly conveyed information may result in misconceptions on the part of the user which can lead to bad decisions or invalid conclusions, which may have costly or even dangerous implications. To address this problem, we analyse human-produced explanations with the aim of transferring explanation expertise to machines. Guided by this analysis, we present a classification of explanatory utterances based on their content and communicative function. We then use these utterance classes and additional text analysis to construct a taxonomy of text types. This text taxonomy characterizes multisentence explanations according to the content they convey, the communicative acts they perform, and their intended effect on the addressee's knowledge, beliefs, goals and plans. We then argue that the act of explanation presentation is an action-based endeavor and introduce and define an integrated theory of communicative acts (rhetorical, illocutionary, and locutionary acts). To illustrate this theory we formalize several of these communicative acts as plan operators and then show their use by a hierarchical text planner (TEXPLAN—Textual EXplanation PLANner) that composes natural language explanations. Finally, we classify a range of reactions readers may have to explanations and illustrate how a system can respond to these given a plan-based approach. Our research thus contributes (1) a domain-independent taxonomy of abstract explanatory utterances, (2) a taxonomy of multisentence explanations based on these utterance classes and (3) a classification of reactions readers may have to explanations as well as (4) an illustration of how these classifications can be applied computationally. Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. We propose a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of rationality: the expectation that agents will plan approximately rationally to achieve their goals, given their beliefs about the world. The mental states that caused an agent’s behavior are inferred by inverting this model of rational planning using Bayesian inference, integrating the likelihood of the observed actions with the prior over mental states. This approach formalizes in precise probabilistic terms the essence of previous qualitative approaches to action understanding based on an ‘‘intentional stance” [Dennett, D. C. (1987). The intentional stance. Cambridge, MA: MIT Press] or a ‘‘teleological stance” [Gergely, G., Nadasdy, Z., Csibra, G., & Biro, S. (1995). Taking the intentional stance at 12 months of age. Cognition, 56, 165–193]. In three psychophysical experiments using animated stimuli of agents moving in simple mazes, we assess how well different inverse planning models based on different goal priors can predict human goal inferences. The results provide quantitative evidence for an approximately rational inference mechanism in human goal inference within our simplified stimulus paradigm, and for the flexible nature of goal representations that human observers can adopt. We discuss the implications of our experimental results for human action understanding in real-world contexts, and suggest how our framework might be extended to capture other kinds of mental state inferences, such as inferences about beliefs, or inferring whether an entity is an intentional agent. We present a modal logic of belief and announcements in a multi-agent setting. This logic allows to express not only that Ψ holds after the announcement of Φ as in standard public announcement logic (PAL), but also that the announcement of Φ occurs. We use the logic to provide a formal analysis of several concepts that are relevant for multi-agent systems (MAS) theory and applications: the notions of communicative action (an agent informs another agent about something) and communicative intention (an agent has the intention to inform another agent about something), and the notion of information source. In this paper we introduce the novel problem of understanding visual persuasion. Modern mass media make extensive use of images to persuade people to make commercial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representations of images, such as detection and identification of objects. However, the pervasive use of images for communicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vision to the higher-level challenge of understanding the underlying communicative intent implied in images. We begin by identifying nine dimensions of persuasive intent latent in images of politicians, such as "socially dominant, " "energetic, " and "trustworthy, " and propose a hierarchical model that builds on the layer of syntactical attributes, such as "smile" and "waving hand, " to predict the intents presented in the images. To facilitate progress, we introduce a new dataset of 1, 124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates that a systematic focus on visual persuasion opens up the field of computer vision to a new class of investigations around mediated images, intersecting with media analysis, psychology, and political communication.