Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Learning recurrent representations for hierarchical behavior modeling

We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules

Braitenberg Vehicles as Developmental Neurosimulation

The connection between brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. Particularly in artificial intelligence research, behavior is generated by a black box approximating the brain. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. This model generates outputs and behaviors from a priori associations, yet this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We will introduce our approach, which is to use Braitenberg Vehicles (BVs) to model the development of an artificial nervous system. The resulting developmental BVs will generate behaviors that range from stimulus responses to group behavior that resembles collective motion. Next, we will situate this work in the domain of artificial brain networks. Then we will focus on broader themes such as embodied cognition, feedback, and emergence. Our perspective will then be exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we will revisit concepts related to our approach and how they might guide future development.Comment: 32 pages, 8 figures, 2 table

To get or to be? Use and acquisition of get- versus be- passives: evidence from children and adults

The use and acquisition of the get-passive has so far yielded a variety of accounts and suggestions. This paper presents new experimental evidence concerning the use and the acquisition of the get-passive by children, as well as adult judgments of get- and be-passives. Within a prototype approach to the passive, experiments investigated when 2–4-year-old British children produce get- as opposed to be-passives. The role of direct affectedness of the patient on get-passive production was investigated further in a follow-up experiment. In addition to the child data, ratings of get- and be-passives were obtained from British English adult speakers to investigate the acceptability of these passives and their relationship to developmental data. The first experiment showed that the chosen prototype approach clearly predicts children’s acquisition of be-passives with get-passives being more peripheral members of the category ‘passive’ than be-passives. The second study shows that even if the child herself is the affected patient in the play action, get-passives are only rarely produced. In contrast to American children, direct affectedness did not induce British children to produce a significant amount of getpassives. Last, adult ratings confirm that British English speakers rate be-passives consistently as better examples of passive sentences than get-passives. The evidence suggests that getpassives are more peripheral for British than for American children and adults. Implications for the possible role of parental input and the validity of existing accounts of the get-passive are discussed