Recreation Resources Center University of Wisconsin-Extension History, Activities, Problems

The Recreation Resources Center (RRC), University of Wisconsin-Extension provides research based educational programming to individuals and communities, with particular emphasis in the Wisconsin recreation-tourism industry. It originated in 1969 as a demonstration project funded by the Upper Great Lakes Regional Commission (UGLRC) to do for the HRT industry what OW-Extension has been doing for farming and agribusiness for several generations. RRC activities include applied research, workshops, and counseling, information dissemination, networking and back up support for Extension Agents. The budget gradually grew, replacing the UGLRC funding with OW-Extension funding, to 8.0 staff. Budget cuts have reduced its staff to the current 5.0 FTE. Future funding efforts will hopefully increase the FTE count

Stellar:A Programming Model for Developing Protocol-Compliant Agents

An interaction protocol captures the rules of encounter in a multiagent system. Development of agents that comply with protocols is a central challenge of multiagent systems. Our contribution in this chapter is a programming model, Stellar, that simplifies development of agents compliant with information protocols specified in BSPL. A significant distinction of Stellar from similar approaches is that it does not rely upon extracting control flow structures from protocol specifications to ensure compliance. Instead, Stellar provides a set of fundamental operations to programmers for producing viable messages according to the correct flow of information between agents as specified by a protocol, enabling flexible design and implementation of protocol-compliant agents. Our main contributions are: (1) identification of a set of programming errors that commonly occur when developing agents for protocol-based multiagent system, (2) definition of Stellar’s operations and a simple yet effective pattern to develop protocol-compliant agents that avoid the identified errors, and (3) demonstration of Stellar’s effectiveness by presenting concrete agents in e-commerce and insurance policy domains

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

The Repast Simulation/Modelling System for Geospatial Simulation

The use of simulation/modelling systems can simplify the implementation of agent-based models. Repast is one of the few simulation/modelling software systems that supports the integration of geospatial data especially that of vector-based geometries. This paper provides details about Repast specifically an overview, including its different development languages available to develop agent-based models. Before describing Repast’s core functionality and how models can be developed within it, specific emphasis will be placed on its ability to represent dynamics and incorporate geographical information. Once these elements of the system have been covered, a diverse list of Agent-Based Modelling (ABM) applications using Repast will be presented with particular emphasis on spatial applications utilizing Repast, in particular, those that utilize geospatial data

Probabilistic inverse reinforcement learning in unknown environments

We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to solve. To do so, we extend previous probabilistic approaches for inverse reinforcement learning in known MDPs to the case of unknown dynamics or opponents. We do this by deriving two simplified probabilistic models of the demonstrator's policy and utility. For tractability, we use maximum a posteriori estimation rather than full Bayesian inference. Under a flat prior, this results in a convex optimisation problem. We find that the resulting algorithms are highly competitive against a variety of other methods for inverse reinforcement learning that do have knowledge of the dynamics.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Teaching agents to learn: from user study to implementation

Graphical user interfaces have helped center computer use on viewing and editing, rather than on programming. Yet the need for end-user programming continues to grow. Software developers have responded to the demand with a barrage of customizable applications and operating systems. But the learning curve associated with a high level of customizability-even in GUI-based operating systems-often prevents users from easily modifying their software. Ironically, the question has become, "What is the easiest way for end users to program?" Perhaps the best way to customize a program, given current interface and software design, is for users to annotate tasks-verbally or via the keyboard-as they are executing them. Experiments have shown that users can "teach" a computer most easily by demonstrating a desired behavior. But the teaching approach raises new questions about how the system, as a learning machine, will correlate, generalize, and disambiguate a user's instructions. To understand how best to create a system that can learn, the authors conducted an experiment in which users attempt to train an intelligent agent to edit a bibliography. Armed with the results of these experiments, the authors implemented an interactive machine learning system, which they call Configurable Instructible Machine Architecture. Designed to acquire behavior concepts from few examples, Cima keeps users informed and allows them to influence the course of learning. Programming by demonstration reduces boring, repetitive work. Perhaps the most important lesson the authors learned is the value of involving users in the design process. By testing and critiquing their design ideas, users keep the designers focused on their objective: agents that make computer-based work more productive and more enjoyable