Characteristics of Small Social Networks

Two dozen networks are analyzed using three parameters that attempt to capture important properties of social networks: leadership L, member bonding B, and diversity of expertise D. The first two of these parameters have antecedents, the third is new. A key part of the analysis is to examine networks at multiple scales by dissecting the entire network into its n subgraphs of a given radius of two edge steps about each of the n nodes. This scale-based analysis reveals constraints on what we have dubbed "cognitive" networks, as contrasted with biological or physical networks. Specifically, "cognitive" networks appear to maximize bonding and diversity over a range of leadership dominance. Asymptotic relations between the bonding and diversity measures are also found when small, nearly complete subgraphs are aggregated to form larger networks. This aggregation probably underlies changes in a regularity among the LBD parameters; this regularity is a U-shaped function of networks size, n, which is minimal for networks around 80 or so nodes

Sidekick agents for sequential planning problems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 127-131).Effective Al sidekicks must solve the interlinked problems of understanding what their human collaborator's intentions are and planning actions to support them. This thesis explores a range of approximate but tractable approaches to planning for AI sidekicks based on decision-theoretic methods that reason about how the sidekick's actions will effect their beliefs about unobservable states of the world, including their collaborator's intentions. In doing so we extend an existing body of work on decision-theoretic models of assistance to support information gathering and communication actions. We also apply Monte Carlo tree search methods for partially observable domains to the problem and introduce an ensemble-based parallelization strategy. These planning techniques are demonstrated across a range of video game domains.by Owen Macindoe.Ph.D

Investigating the fine grained structure of networks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 107-109).In this thesis I explore a novel representation for characterizing a graph's fine grained structure. The key idea is that this structure can be represented as a distribution of the structural features of subgraphs. I introduce a set of such structural features and use them to compute representations for a variety of graphs, demonstrating their use in qualitatively describing fine structure. I then demonstrate the utility of this representation with quantitative techniques for computing graph similarity and graph clustering. I show that similarity judged using this representation is significantly different from judgements using full graph structural measures. I find that graphs from the same class of networks, such as email correspondence graphs, can differ significantly in their fine structure across the institutions whose relations they model, but also find examples of graphs from the same institutions across different time periods that share a similar fine structure.by Owen Macindoe.S.M

Help or hinder: Bayesian models of social goal inference

Everyday social interactions are heavily influenced by our snap judgments about others’ goals. Even young infants can infer the goals of intentional agents from observing how they interact with objects and other agents in their environment: e.g., that one agent is ‘helping’ or ‘hindering’ another’s attempt to get up a hill or open a box. We propose a model for how people can infer these social goals from actions, based on inverse planning in multiagent Markov decision problems (MDPs). The model infers the goal most likely to be driving an agent’s behavior by assuming the agent acts approximately rationally given environmental constraints and its model of other agents present. We also present behavioral evidence in support of this model over a simpler, perceptual cue-based alternative.United States. Army Research Office (ARO MURI grant W911NF-08-1-0242)United States. Air Force Office of Scientific Research (MURI grant FA9550-07-1-0075)National Science Foundation (U.S.) (Graduate Research Fellowship)James S. McDonnell Foundation (Collaborative Interdisciplinary Grant on Causal Reasoning

Assistant Agents for Sequential Planning Problems

The problem of optimal planning under uncertainty in collaborative multi-agent domains is known to be deeply intractable but still demands a solution. This thesis will explore principled approximation methods that yield tractable approaches to planning for AI assistants, which allow them to understand the intentions of humans and help them achieve their goals. AI assistants are ubiquitous in video games, mak- ing them attractive domains for applying these planning techniques. However, games are also challenging domains, typically having very large state spaces and long planning horizons. The approaches in this thesis will leverage recent advances in Monte-Carlo search, approximation of stochastic dynamics by deterministic dynamics, and hierarchical action representation, to handle domains that are too complex for existing state of the art planners. These planning techniques will be demonstrated across a range of video game domains

The Coevolution of Punishment and Prosociality Among Learning Agents

We explore the coevolution of punishment and prosociality in a population of learning agents. Across three models, we find that the capacity to learn from punishment can allow both punishment and prosocial behavior to evolve by natural selection. In order to model the effects of innate behavioral dispositions (such as prosociality) combined with the effects of learning (such as a response to contingent punishment), we adopt a Bayesian framework. Agents choose actions by considering their probable outcomes, calculated from an innate, heritable prior distribution and agents ’ experience of actual outcomes. We explore models in which an agent learns about the dispositions of each individual agent independently, as well as models in which an agent combines individual-level and group-level learning. Our results illustrate how the integration of Bayesian cognitive models into agent-based simulations of natural selection can reveal evolutionary dynamics in the optimal balance between innate knowledge and learning

Intrinsically Motivated Intelligent Rooms

Abstract. Intelligent rooms are responsive environments in which human activities are monitored and responses are generated to facilitate these activities. Research and development on intelligent rooms currently focuses on the integration of multiple sensor devices with pre-programmed responses to specific triggers. Developments in intelligent agents towards intrinsically motivated learning agents can be integrated with the concept of an intelligent room. The resulting model focuses developments in intelligent rooms on a characteristic reasoning process that uses motivation to guide action and learning. Using a motivated learning agent model as the basis for an intelligent room opens up the possibility of intelligent environments being able to adapt both to people’s changing usage patterns and to the addition of new capabilities, via the addition of new sensors and effectors, with relatively little need for reconfiguration by humans.