Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.

The theory of mind is an important human capability that allows us to understand and predict the goals, intents, and beliefs of other individuals. We present an approach to designing intelligent communicative agents based on modeling theories of mind. This can be tricky because other agents may also have their own theories of mind of the first agent, meaning that these mental models are naturally nested in layers. So, to look for intuitive communicative acts, we recursively apply a planning algorithm in each of these nested layers, looking for possible plans of action as well as their hypothetical consequences, which include the reactions of other agents; we propose that truly intelligent communicative acts are the ones which produce a state of maximum decision theoretic utility according to the entire theory of mind. We implement these ideas using Java and OpenCyc in an attempt to create an assistive AI we call MARTHA. We demonstrate MARTHA's capabilities with two motivating examples: helping the user buy a sandwich and helping the user search for an activity. We see that, in addition to being a personal assistant, MARTHA can be extended to other assistive fields, such as finance, research, and government.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.

Uninformed Search: This is used when creating an action sequence that doesn't account for any changes along the way. Heuristic Functions: These allow for decisions to be made without accurate or complete information. Adversarial or Moving Agent Search: This is used when there are other entities making decisions that influence one another. Piotr Gmytrasiewicz, associate professor in the department of computer science at the University of Illinois at Chicago, teaches three courses: Artificial Intelligence 1, Artificial Intelligence 2 and Applied Artificial Intelligence. Artificial Intelligence 1 covers logic-based approaches, while Artificial Intelligence 2 showcases numerical and mathematically focused approaches based on probability theory.

Interactive dynamic influence diagrams(I-DIDs) are a well recognized decision model that explicitly considers how multiagent interaction affects individual decision making. To predict behavior of other agents, I-DIDs require models of the other agents to be known ahead of time and manually encoded. This becomes a barrier to I-DID applications in a human-agent interaction setting, such as development of intelligent non-player characters(NPCs) in real-time strategy(RTS) games, where models of other agents or human players are often inaccessible to domain experts. In this paper, we use automatic techniques for learning behavior of other agents from replay data in RTS games. We propose a learning algorithm with improvement over existing work by building a full profile of agent behavior. This is the first time that data-driven learning techniques are embedded into the I-DID decision making framework. We evaluate the performance of our approach on two test cases.

Decision making is a key feature of autonomous systems. It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations. I conclude by examining the avenues that research pertaining to these frameworks is pursuing.

We present a novel methodology for decision-making by computer agents that leverages a computational concept of emotions. It is believed that emotions help living organisms perform well in complex environments. Can we use them to improve the decision-making performance of computer agents? We explore this possibility by formulating emotions as mathematical operators that serve to update the relative priorities of the agent's goals. The agent uses rudimentary domain knowledge to monitor the expectation that its goals are going to be accomplished in the future, and reacts to changes in this expectation by "experiencing emotions." The end result is a projection of the agent's long-run utility function, which might be too complex to optimize or even represent, to a time-varying valuation function that is being myopically maximized by selecting appropriate actions. Our methodology provides a systematic way to incorporate emotion into a decision-theoretic framework, and also provides a principled, domain-independent methodology for generating heuristics in novel situations. We test our agents in simulation in two domains: restless bandits and a simple foraging environment. Our results indicate that emotion-based agents outperform other reasonable heuristics for such difficult domains, and closely approach computationally expensive near-optimal solutions, whenever these are computable, yet requiring only a fraction of the cost.