Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.Comment: Final manuscript (46 pages), published in Artificial Intelligence Journal. The arXiv version also contains a table of contents after the abstract, but is otherwise identical to the AIJ version. Keywords: autonomous agents, multiagent systems, modelling other agents, opponent modellin

Use of agent – based models in characterizing farm types and evolvement in smallholder dairy systems

A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe ever-increasing demand for milk and dairy products has attracted research interventions on how milk yield can be increased for the context of smallholder farmers. While bearing significant contribution on milk production and fulfilment of the market demand, the smallholder dairy farmers are faced with challenges that hinder productivity. Among the challenges is the inadequate characterization of the dairy production systems and lack of knowledge on factors attributing to their growth. This has resulted in aggregation of the smallholder dairy farmers and lack of interventions tailored to suit particular farm types. By using Tanzania and Ethiopia as case studies, this research identified the main determinants for evolvement of smallholder dairy farmers. Evolvement in this research refers to, gradual increase in milk yield. The factors that determine evolvement for individual farm typologies were identified by using cluster and frequent pattern analysis. The differential influence of the identified determinants towards increase in milk yield was studied by using Agent-based modelling and simulation where each factor was observed. Six farm types were identified for Tanzania and four for Ethiopia. The characteristics of the farm types were enriched by frequent pattern analysis with confidence level 60% - 97%. Agentbased modelling revealed that, income and farm-based determinants influenced an increase of up to 7.58 litres above the average (13.62 ± 4.47) for Ethiopia. For Tanzania, farm and farmerbased determinants influenced an increase of up to 7.72 litres of milk above the average (12.7 ± 4.89). The identified determinants could predict up to 96% and 93% of the variances in milk yield for Tanzania and Ethiopia, respectively. There was an increase in milk yield based on the identified evolvement determinants; from baseline data average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. Dairy development agencies should consider the disaggregation of dairy farmers and prioritization of the determinants identified in this research for evolvement of dairy farms. In future, it is important to develop a web or mobile application that can inform smallholder dairy farmers about the identified evolvement determinants to aid on-farm decision making

Bayesian theory of mind

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 127-139).This thesis proposes a computational framework for understanding human Theory of Mind (ToM): our conception of others' mental states, how they relate to the world, and how they cause behavior. Humans use ToM to predict others' actions, given their mental states, but also to do the reverse: attribute mental states - beliefs, desires, intentions, knowledge, goals, preferences, emotions, and other thoughts - to explain others' behavior. The goal of this thesis is to provide a formal account of the knowledge and mechanisms that support these judgments. The thesis will argue for three central claims about human ToM. First, ToM is constructed around probabilistic, causal models of how agents' beliefs, desires and goals interact with their situation and perspective (which can differ from our own) to produce behavior. Second, the core content of ToM can be formalized using context-specific models of approximately rational planning, such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), and Markov games. ToM reasoning will be formalized as rational probabilistic inference over these models of intentional (inter)action, termed Bayesian Theory of Mind (BToM). Third, hypotheses about the structure and content of ToM can be tested through a combination of computational modeling and behavioral experiments. An experimental paradigm for eliciting fine-grained ToM judgments will be proposed, based on comparing human inferences about the mental states and behavior of agents moving within simple two-dimensional scenarios with the inferences predicted by computational models. Three sets of experiments will be presented, investigating models of human goal inference (Chapter 2), joint belief-desire inference (Chapter 3), and inference of interactively-defined goals, such as chasing and fleeing (Chapter 4). BToM, as well as a selection of prominent alternative proposals from the social perception literature will be evaluated by their quantitative fit to behavioral data. Across the present experiments, the high accuracy of BToM, and its performance relative to alternative models, will demonstrate the difficulty of capturing human social judgments, and the success of BToM in meeting this challenge.by Chris L. Baker.Ph.D