Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on RFSs with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vector-type BP is a special case of set-type BP, where each RFS follows the Bernoulli process. To demonstrate the validity of developed set-type BP, we apply it to the PMB filter for SLAM, which naturally leads to new set-type BP-mapping, SLAM, multi-target tracking, and simultaneous localization and tracking filters. Finally, we explore the relationships between the vector-type BP and the proposed set-type BP PMB-SLAM implementations and show a performance gain of the proposed set-type BP PMB-SLAM filter in comparison with the vector-type BP-SLAM filter.

We present DyFOS, an active perception method that dynamically finds optimal states to minimize localization uncertainty while avoiding obstacles and occlusions. We consider the scenario where a perception-denied rover relies on position and uncertainty measurements from a viewer robot to localize itself along an obstacle-filled path. The position uncertainty from the viewer's sensor is a function of the states of the sensor itself, the rover, and the surrounding environment. To find an optimal sensor state that minimizes the rover's localization uncertainty, DyFOS uses a localization uncertainty prediction pipeline in an optimization search. Given numerous samples of the states mentioned above, the pipeline predicts the rover's localization uncertainty with the help of a trained, complex state-dependent sensor measurement model (a probabilistic neural network). Our pipeline also predicts occlusion and obstacle collision to remove undesirable viewer states and reduce unnecessary computations. We evaluate the proposed method numerically and in simulation. Our results show that DyFOS is faster than brute force yet performs on par. DyFOS also yielded lower localization uncertainties than faster random and heuristic-based searches.

Empowerment -- a domain independent, information-theoretic metric -- has previously been shown to assist in the evolutionary search for neural cellular automata (NCA) capable of homeostasis when employed as a fitness function. In our previous study, we successfully extended empowerment, defined as maximum time-lagged mutual information between agents' actions and future sensations, to a distributed sensorimotor system embodied as an NCA. However, the time-delay between actions and their corresponding sensations was arbitrarily chosen. Here, we expand upon previous work by exploring how the time scale at which empowerment operates impacts its efficacy as an auxiliary objective to accelerate the discovery of homeostatic NCAs. We show that shorter time delays result in marked improvements over empowerment with longer delays, when compared to evolutionary selection only for homeostasis. Moreover, we evaluate stability and adaptability of evolved NCAs, both hallmarks of living systems that are of interest to replicate in artificial ones. We find that short-term empowered NCA are more stable and are capable of generalizing better to unseen homeostatic challenges. Taken together, these findings motivate the use of empowerment during the evolution of other artifacts, and suggest how it should be incorporated to accelerate evolution of desired behaviors for them. Source code for the experiments in this paper can be found at: https://github.com/caitlingrasso/empowered-nca-II.

In prior work, learning from demonstration (LfD) [1, 2] has successfully enabled robots to accomplish multi-step tasks by segmenting demonstrations (primarily of robot end-effector or tool trajectories) into sub-tasks/goals [3, 4, 5, 6, 7, 8], phases [9, 10], keyframes [11, 12], or skills/primitives/options [13, 14, 15, 16]. Most of these abstractions assume reaching subgoals sequentially will deliver the desired outcomes; however, successful imitation of many manipulation tasks with spatial/temporal constraints cannot be reduced to imitation at the motion level unless the learned motion policy also satisfies these constraints. This becomes highly relevant if we want robots to not only imitate but also generalize, adapt and be robust to perturbations imposed by humans, who are in the loop of task learning and execution. LfD techniques that learn stable motion policies with convergence guarantees (e.g., Dynamic Movement Primitives (DMP) [17], Dynamical Systems (DS) [18]) are capable of providing such desired properties but only at the motion level. As shown in Figure 1 (a-b) a robot can successfully replay a soup-scooping task while being robust to physical perturbations with a learned DS. Nevertheless, if the spoon orientation is perturbed to a state where all material is dropped, as seen in Figure 1 (c), the motion policy will still lead the robot to the target, unaware of the task-level failure or how to recover from it.

Reinforcement Learning (RL) is known to be often unsuccessful in environments with sparse extrinsic rewards. A possible countermeasure is to endow RL agents with an intrinsic reward function, or 'intrinsic motivation', which rewards the agent based on certain features of the current sensor state. An intrinsic reward function based on the principle of empowerment assigns rewards proportional to the amount of control the agent has over its own sensors. We implemented a variation on a recently proposed intrinsically motivated agent, which we refer to as the 'curious' agent, and an empowerment-inspired agent. The former leverages sensor state encoding with a variational autoencoder, while the latter predicts the next sensor state via a variational information bottleneck. We compared the performance of both agents to that of an advantage actor-critic baseline in four sparse reward grid worlds. Both the empowerment agent and its curious competitor seem to benefit to similar extents from their intrinsic rewards. This provides some experimental support to the conjecture that empowerment can be used to drive exploration.

Abstract-- Creating Non-Player Characters (NPCs) that can react robustly to unforeseen player behaviour or novel game content is difficult and time-consuming. This hinders the design of believable characters, and the inclusion of NPCs in games that rely heavily on procedural content generation. We have previously addressed this challenge by means of empowerment, a model of intrinsic motivation, and demonstrated how a coupled empowerment maximisation (CEM) policy can yield generic, companion-like behaviour . In this paper, we extend the CEM framework with a minimisation policy to give rise to adversarial behaviour . We conduct a qualitative, exploratory study in a dungeon-crawler game, demonstrating that CEM can exploit the affordances of different content facets in adaptive adversarial behaviour without modifications to the policy. Changes to the level design, underlying mechanics and our character's actions do not threaten our NPC's robustness, but yield new and surprising ways to be mean. Non-Player Characters (NPCs) in video games serve many purposes: they can be quest givers, conversation partners, leaders, sidekicks or other kinds of collaborators [1]. But in many cases they are adversaries . Adversarial NPCs also come in many forms, their behaviour varying according to the game genre, the design affordances, and the underlying algorithms. Treanor et al. [2] make the fundamental distinction between AI as Adversary and AI as Villain. Adversaries are designed to defeat the player without resorting to cheating, e.g. an AI for Chess or Go. The objective of an NPC villain in contrast is not to defeat the player but to create an interesting challenge which can be overcome eventually. We refer to both types simply as adversaries.

The cognitive agent model presented in this paper generates  prior and retrospective ownership states for an action based on principles from recent neuro-logical theories. A prior ownership state is affected by prediction of the effects of a prepared action, and exerts control by strengthening or suppressing actual execution of the action. A retrospective ownership state depends on whether the sensed consequences co-occur with the predicted consequences, and is the basis for acknowledging authorship of actions, for example, in social context. It is shown how poor action effect prediction capabilities can lead to reduced retrospective ownership states, as in persons suffering from schizophrenia.

Differences in social responses of individuals can often be related to differences in functioning of neurological mechanisms. This paper presents a cognitive agent model capable of showing different types of social response patterns based on such mechanisms, adopted from theories on mirror neuron systems, emotion regulation, empathy, and autism spectrum disorders. The presented agent model provides a basis for human-like social response patterns of virtual agents in the context of simulation-based training (e.g., for training of therapists), gaming, or for agent-based generation of virtual stories.