Social cognition depends on our capacity for ‘mentalizing’, or explaining an agent’s behaviour in terms of their mental states. The development and neural substrates of mentalizing are well-studied, but its computational basis is only beginning to be probed. Here we present a model of core mentalizing computations: inferring jointly an actor’s beliefs, desires and percepts from how they move in the local spatial environment. Our Bayesian theory of mind (BToM) model is based on probabilistically inverting artificial-intelligence approaches to rational planning and state estimation, which extend classical expected-utility agent models to sequential actions in complex, partially observable domains. The model accurately captures the quantitative mental-state judgements of human participants in two experiments, each varying multiple stimulus dimensions across a large number of stimuli. Comparative model fits with both simpler ‘lesioned’ BToM models and a family of simpler non-mentalistic motion features reveal the value contributed by each component of our model. A Bayesian theory of mind model is shown to infer and quantify the mental state and judgements of humans in decision-making scenarios. The model is a key step towards enabling machines to ‘intuit’ human thoughts and desires.

Rational quantitative attribution of beliefs, desires and percepts in human mentalizing

The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves. Disparate streams of research have approached non-stationarity from several angles, which make a variety of implicit assumptions that make it hard to keep an overview of the state of the art and to validate the innovation and significance of new works. This survey presents a coherent overview of work that addresses opponent-induced non-stationarity with tools from game theory, reinforcement learning and multi-armed bandits. Further, we reflect on the principle approaches how algorithms model and cope with this non-stationarity, arriving at a new framework and five categories (in increasing order of sophistication): ignore, forget, respond to target models, learn models, and theory of mind. A wide range of state-of-the-art algorithms is classified into a taxonomy, using these categories and key characteristics of the environment (e.g., observability) and adaptation behaviour of the opponents (e.g., smooth, abrupt). To clarify even further we present illustrative variations of one domain, contrasting the strengths and limitations of each category. Finally, we discuss in which environments the different approaches yield most merit, and point to promising avenues of future research.

A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: personal agent assistants, computer and network security, opponent modeling in games and simulation systems, coordination in robots and software agents, web e-commerce and collaborative filtering, dialog modeling, video surveillance, smart homes In this book, follow the history of this research area and witness exciting new developments in the field made possible by improved sensors, increased computational power, and new application areas. Combines basic theory on algorithms for plan/activity recognition along with results from recent workshops and seminars Explains how to interpret and recognize plans and activities from sensor data Provides valuable background knowledge and assembles key concepts into one guide for researchers or students studying these disciplines

Plan, Activity, and Intent Recognition: Theory and Practice

Belief and truth in hypothesised behaviours

Theory of mind refers to the ability to reason explicitly about unobservable mental content of others, such as beliefs, goals, and intentions. People often use this ability to understand the behavior of others as well as to predict future behavior. People even take this ability a step further, and use higher-order theory of mind by reasoning about the way others make use of theory of mind and in turn attribute mental states to different agents. One of the possible explanations for the emergence of the cognitively demanding ability of higher-order theory of mind suggests that it is needed to deal with mixed-motive situations. Such mixed-motive situations involve partially overlapping goals, so that both cooperation and competition play a role. In this paper, we consider a particular mixed-motive situation known as Colored Trails, in which computational agents negotiate using alternating offers with incomplete information about the preferences of their trading partner. In this setting, we determine to what extent higher-order theory of mind is beneficial to computational agents. Our results show limited effectiveness of first-order theory of mind, while second-order theory of mind turns out to benefit agents greatly by allowing them to reason about the way they can communicate their interests. Additionally, we let human participants negotiate with computational agents of different orders of theory of mind. These experiments show that people spontaneously make use of second-order theory of mind in negotiations when their trading partner is capable of second-order theory of mind as well.

/pdf/negotiating-with-other-minds-the-role-of-recursive-theory-of-1ohau1hgsg.pdf

Negotiating with other minds: the role of recursive theory of mind in negotiation with incomplete information

Recursive reasoning of the form what do I think that you think that I think (and so on) arises often while acting rationally in multiagent settings. Several multiagent decision-making frameworks such as RMM, I-POMDP and the theory of mind model recursive reasoning as integral to an agent's rational choice. Real-world application settings for multiagent decision making are often mixed involving humans and human-controlled agents. In two large experiments, we studied the level of recursive reasoning generally displayed by humans while playing sequential general-sum and filed-sum, two-player games. Our results show that subjects experiencing a general-sum strategic game display first or second level of recursive thinking with the first level being more prominent. However, if the game is made simpler and more competitive with filed-sum payoffs, subjects predominantly attributed first-level recursive thinking to opponents thereby acting using second level of reasoning. Subsequently, we model the behavioral data obtained from the studies using the I-POMDP framework, appropriately augmented using well-known human judgment and decision models. Accuracy of the predictions by our models suggest that these could be viable ways for computationally modeling strategic behavioral data.

/pdf/modeling-recursive-reasoning-by-humans-using-empirically-rpwzki94ko.pdf

Modeling recursive reasoning by humans using empirically informed interactive POMDPs

Recursive reasoning of the form what do I think that you think that I think (and so on) arises often while acting rationally in multiagent settings. Previous investigations indicate that humans do not tend to ascribe recursive thinking to others. Several multiagent decisionmaking frameworks such as RMM, I-POMDP and the theory of mind model recursive reasoning as integral to an agent’s rational choice. Real-world application settings for multiagent decision making tend to be mixed involving humans and human-controlled agents. We investigate recursive reasoning exhibited by humans during strategic decision making. In a large experiment involving 162 participants, we studied the level of recursive reasoning generally displayed by humans while playing a sequential fixedsum, two-player game. Our results show that subjects experiencing a strategic game made more competitive with fixed-sum payoffs and tangible incentives predominantly attributed first-level recursive thinking to opponents. They acted using second level of reasoning exceeding levels of reasoning observed previously.

Two Level Recursive Reasoning by Humans Playing Sequential Fixed-Sum Games

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Modeling recursive reasoning by humans using empirically informed interactive POMDPs

Two Level Recursive Reasoning by Humans Playing Sequential Fixed-Sum Games