Philip E. AgreMIT Press, 1996
University of California, San Diego
Stanley J. Rosenschein
The field of artificial intelligence has developed an "agent perspective",
expanding its focus from thought to action, from search spaces to physical
environments, and from problem-solving to long-term activity. Originally
published in February 1995 as volumes 72 and 73 of the journal Artificial
Intelligence, this book brings together seventeen papers by researchers
in artificial intelligence, neural networks, computer science, robotics,
and cognitive science on the themes of interaction and agency. Each paper
develops a distinct conceptual framework for the principled characterization
of interactions between agents and their environments, as well as the use
of such characterizations to guide the analysis of existing agents and the
synthesis of artificial agents.
Each abstract is accompanied by its full citation as a journal article.
Each abstract is accompanied by its full citation as a journal article.
Computational Research on Interaction and Agency
Philip E. Agre Department of Communication University of California, San Diego La Jolla, California 92093-0503 email@example.comRecent research in artificial intelligence has developed computational theories of agents' involvements in their environments. Although inspired by a great diversity of formalisms and architectures, these research projects are unified by a common concern: using principled characterizations of agents' interactions with their environments to guide analysis of living agents and design of artificial ones. This article offers a conceptual framework for such theories, surveys several other fields of research that hold the potential for dialogue with these new computational projects, and summarizes the principal contributions of the articles in this special double volume. It also briefly describes a case study in these ideas -- a computer program called Toast that acts as a short-order breakfast cook. Because its designers have discovered useful structures in the world it inhabits, Toast can employ an extremely simple mechanism to decide what to do next.
Full citation: Philip E. Agre, Computational research on interaction and agency, Artificial Intelligence 72(1-2), 1995, pages 1-52.
Sensorimotor Transformations in the Worlds of Frogs and Robots
Michael Arbib Center for Neural Engineering University of Southern California Los Angeles, California 90089-2520 firstname.lastname@example.org Jim-Shih Liaw Center for Neural Engineering University of Southern California Los Angeles, California 90089-2520 email@example.comThe paper develops a multilevel approach to the design and analysis of systems with "action-oriented perception", situating various robot and animal "designs" in an evolutionary perspective. We present a set of biological design principles within a broader perspective that shows their relevance for robot design. We introduce schemas to provide a coarse-grain analysis of "cooperative computation" in the brains of animals and the "brains" of robots, starting with an analysis of approach, avoidance, detour behavior, and path planning in frogs. An explicit account of neural mechanism of avoidance behavior in the frog illustrates how schemas may be implemented in neural networks. The focus of the rest of the article is on the relation of instinctive to reflective behavior. We generalize an analysis of the interaction of perceptual schemas in the VISIONS system for computer vision to a view of the interaction of perceptual and motor schemas in distributed planning which, we argue, has great promise for integrating mechanisms for action and perception in both animal and robot. We conclude with general observations on the lessons on relating structure and function which can be carried from biology to technology.
Full citation: Michael A. Arbib and Jim-Shih Liaw, Sensorimotor transformations in the worlds of frogs and robots, Artificial Intelligence 72(1-2), 1995, pages 53-79.
Learning to Act Using Real-Time Dynamic Programming
Andrew G. Barto Computer Science Department, LGRC University of Massachusetts Amherst, Massachusetts 01003-4610 firstname.lastname@example.org Steven J. Bradtke GTE Data Services One E Telecom Parkway, DC F4M Temple Terrace, Florida 33637 email@example.com Satinder Pal Singh Department of Brain and Cognitive Sciences Massachusetts Institute of Technology, E10-243 Cambridge, Massachusetts 02139 firstname.lastname@example.orgLearning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the system being controlled is incompletely known. We introduce an algorithm based on DP, which we call Real-Time DP (RTDP), by which an embedded system can improve its performance with experience. RTDP generalizes Korf's Learning-Real-Time-A* algorithm to problems involving uncertainty. We invoke results from the theory of asynchronous DP to prove that RTDP achieves optimal behavior in several different classes of problems. We also use the theory of asynchronous DP to illuminate aspects of other DP-based reinforcement learning methods such as Watkins' Q-Learning algorithm. A secondary aim of this article is to provide a bridge between AI research on real-time planning and learning and relevant concepts and algorithms from control theory.
Full citation: Andrew G. Barto, Steven J. Bradtke, and Satinder P. Singh, Learning to act using real-time dynamic programming, Artificial Intelligence 72(1-2), 1995, pages 81-138.
System Identification for Perceptually Challenged Agents
Kenneth Basye Department of Math and Computer Science Clark University 950 Main Street Worcester, Massachusetts 01610 email@example.com Tom Dean Box 1910, Computer Science Department Brown University Providence, Rhode Island 02912 firstname.lastname@example.org Leslie Pack Kaelbling Box 1910, Computer Science Department Brown University Providence, Rhode Island 02912 email@example.comFrom the perspective of an agent, the input/output behavior of the environment in which it is embedded can be described as a dynamical system. Inputs correspond to the actions executable by the agent in making transitions between states of the environment. Outputs correspond to the perceptual information available to the agent in particular states of the environment. We view dynamical system identification as inference of deterministic finite-state automata from sequences of input/output pairs. The agent can influence the sequence of input/output pairs it is presented by pursuing a strategy for exploring the environment. We identify two sorts of perceptual errors: errors in perceiving the output of a state and errors in perceiving the inputs actually carried out in making a transition from one state to another. We present efficient, high-probability learning algorithms for a number of system identification problems involving such errors. We also present the results of empirical investigations applying these algorithms to learning spatial representations.
Full citation: Ken Basye, Tom Dean, and Leslie Pack Kaelbling, Learning dynamics: System identification for perceptually challenged agents, Artificial Intelligence 72(1-2), 1995, pages 139-171.
A Dynamical Systems Perspective on Agent-Environment Interaction
Randall D. Beer Department of Computer Engineering and Science Case Western Reserve University Cleveland, Ohio 44106-7071 firstname.lastname@example.orgUsing the language of dynamical systems theory, a general theoretical framework for the synthesis and analysis of autonomous agents is sketched. In this framework, an agent and its environment are modeled as two coupled dynamical systems whose mutual interaction is in general jointly responsible for the agent's behavior. In addition, the adaptive fit between an agent and its environment is characterized in terms of the satisfaction of a given constraint on the trajectories of the coupled agent-environment system. The utility of this framework is demonstrated by using it to first synthesize and then analyze a walking behavior for a legged agent.
Full citation: Randall D. Beer, A dynamical systems perspective on agent-environment interaction, Artificial Intelligence 72(1-2), 1995, pages 173-215.
On Information Invariants in Robots
Bruce Randall Donald Computer Science Department Cornell University Ithaca, New York 14850 email@example.comWe consider the problem of determining the information requirements to perform robot tasks, using the concept of information invariants. This paper represents our attempt to characterize a family of complicated and subtle issues concerned with measuring robot task complexity. We also provide a first approximation to a purely operational theory that addresses a narrow but interesting special case.
We discuss several measures for the information complexity of a task: (a) How much internal state should the robot retain? (b) How many cooperating agents are required, and how much communication between them is necessary? (c) How can the robot change (side-effect) the environment in order to record state or sensory information to perform a task? (d) How much information is provided by sensors? and (e) How much computation is required by the robot? We consider how one might develop a kind of "calculus" on (a) -- (e) in order to compare the power of sensor systems analytically. To this end, we attempt to develop a notion of information invariants. We develop a theory whereby one sensor can be "reduced" to another (much in the spirit of computation-theoretic reductions), by adding, deleting, and reallocating (a) -- (e) among collaborating autonomous agents.
Full citation: Bruce R. Donald, On information invariants in robotics, Artificial Intelligence 72(1-2), 1995, pages 217-304.
The Stabilization of Environments
Kristian J. Hammond Computer Science Department University of Chicago 1100 East 58th Street Chicago, Illinois 60637 firstname.lastname@example.org Timothy M. Converse Computer Science Department University of Chicago 1100 East 58th Street Chicago, Illinois 60637 email@example.com Joshua W. Grass Computer Science Department University of Chicago 1100 East 58th Street Chicago, Illinois 60637 firstname.lastname@example.orgIn planning and activity research there are two common approaches to matching agents with environments. Either the agent is designed with a specific environment in mind, or it is provided with learning capabilities so that it can adapt to the environment it is placed in. In this paper we look at a third and underexploited alternative: designing agents which adapt their environments to suit themselves. We call this stabilization, and we present a taxonomy of types of stability that human beings typically both rely on and enforce. We also taxonomize the ways in which stabilization behaviors can be cued and learned. We illustrate these ideas with a program called FixPoint, which improves its performance over time by stabilizing its environment.
Full citation: Kristian J. Hammond, Timothy M. Converse, and Joshua W. Grass, The stabilization of environments, Artificial Intelligence 72(1-2), 1995, pages 305-327.
An Architecture for Adaptive Intelligent Systems
Barbara Hayes-Roth Knowledge Systems Laboratory Department of Computer Science Stanford University Stanford, California 94305 email@example.comOur goal is to understand and build comprehensive agents that function effectively in challenging niches. In particular, we identify a class of niches to be occupied by "adaptive intelligent systems (AISs)". In contrast with niches occupied by typical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria. We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper. To function effectively throughout the range of situations presented by an AIS niche, an agent must be highly adaptive. In contrast with the rather stereotypic behavior of typical AI agents, an AIS must adapt several key aspects of its behavior to its dynamic situation: its perceptual strategy, its control mode, its choices of reasoning tasks to perform, its choices of reasoning methods for performing chosen tasks; and its meta-control strategy for global coordination of all its behavior. We have designed and implemented an agent architecture that supports all of these different kinds of adaptation by exploiting a single underlying theoretical concept: An agent dynamically constructs explicit control plans to guide its choices among situation-triggered behaviors. The architecture has been used to build experimental agents for several AIS niches. We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring.
Full citation: Barbara Hayes-Roth, An architecture for adaptive intelligent systems, Artificial Intelligence 72(1-2), 1995, pages 329-365.
Analysis of Adaptation and Environment
Ian Horswill MIT Artificial Intelligence Laboratory 545 Technology Square Cambridge, Massachusetts 02139 firstname.lastname@example.orgDesigners often improve the performance of artificial agents by specializing them. We can make a rough, but useful distinction between specialization to a task and specialization to an environment. Specialization to an environment can be difficult to understand: it may be unclear on what properties of the environment the agent depends, or in what manner it depends on each individual property. In this paper, I discuss a method for analyzing specialization into a series of conditional optimizations: formal transformations which, given some constraint on the environment, map mechanisms to more efficient mechanisms with equivalent behavior. I apply the technique to the analysis of the vision and control systems of a working robot system in day to day use in our laboratory.
The method is not intended as a general theory for automated synthesis of arbitrary specialized agents. Nonetheless, it can be used to perform post-hoc analysis of agents so as to make explicit the environment properties required by the agent and the computational value of each property. This post-hoc analysis helps explain performance in normal environments and predict performance in novel environments. In addition, the transformations brought out in the analysis of one system can be reused in the synthesis of future systems.
Full citation: Ian Horswill, Analysis of adaptation and environment, Artificial Intelligence 73(1-2), 1995, pages 1-30.
The Intelligent Use of Space
David Kirsh Department of Cognitive Science University of California, San Diego La Jolla, California 92093-0515 email@example.comThe objective of this essay is to provide the beginning of a principled classification of some of the ways space is intelligently used. Studies of planning have typically focused on the temporal ordering of action, leaving as unaddressed, questions of where to lay down instruments, ingredients, work-in-progress, and the like. But, in having a body, we are spatially located creatures: we must always be facing some direction, have only certain objects in view, be within reach of certain others. How we manage the spatial arrangement of items around us, is not an afterthought; it is an integral part of the way we think, plan and behave. The proposed classification has three main categories: spatial arrangements that simplify choice; spatial arrangements that simplify perception; and spatial dynamics that simplify internal computation. The data for such a classification is drawn from videos of cooking, assembly and packing, everyday observations in supermarkets, workshops and playrooms, and experimental studies of subjects playing Tetris, the computer game. This study, therefore, focusses on interactive processes in the medium and short term: on how agents set up their workplace for particular tasks, and how they continuously manage that workplace.
Full citation: David Kirsh, The intelligent use of space, Artificial Intelligence 73(1-2), 1995, pages 31-68.
Indexical Knowledge and Robot Action: A Logical Account
Yves Lespérance Department of Computer Science University of Toronto 6 Kings College Road, Room 283 Toronto, Ontario M5S 1A4 Canada firstname.lastname@example.org Hector J. Levesque Department of Computer Science University of Toronto 6 Kings College Road, Room 283 Toronto, Ontario M5S 1A4 Canada email@example.comThe knowledge required for action is generally indexical rather than objective. For example, a robot that knows the relative position of an object is generally able to go and pick it up; he need not know its absolute position. Agents may have very incomplete knowledge of their situation in terms of what objective facts hold and still be able to achieve their goals. This paper presents a formal theory of knowledge and action, embodied in a modal logic, that handles the distinction between indexical and objective knowledge and allows a proper specification of the knowledge prerequisites and effects of action. Several kinds of robotics situations involving indexical knowledge are formalized within the framework; these examples show how actions can be specified so as to avoid making excessive requirements upon the knowledge of agents. Various other papers on related issues can be found in our repository.
Full citation: Yves Lespérance and Hector J. Levesque, Indexical knowledge and robot action: A logical account, Artificial Intelligence 73(1-2), 1995, pages 69-115.
Exploiting Patterns of Interaction to Achieve Reactive Behavior
Damian Lyons A. J. Hendriks Philips Laboratories Philips Electronics North America Corporation Briarcliff Manor, New York 10510 firstname.lastname@example.orgThis paper introduces an approach that allows an agent to exploit inherent patterns of interaction in its environment, so-called dynamics, to achieve its objectives. The approach extends the standard treatment of planning and (re)action in which part of the input to the plan generation algorithm is a set of basic actions and perhaps some domain axioms. Real world actions are typically difficult to categorize consistently and are highly context dependent. The approach presented here takes as input a procedural model of the agent's environment and produces as output a set of action descriptions that capture how the agent can exploit the dynamics in the environment. An agent constructed with this approach can utilize context sensitive actions, "servo" style actions, and other intuitively efficient ways to manipulate its environment.
A process-algebra based representation, RS, is introduced to model the environment and the agent's reactions. The paper demonstrates how to analyze an RS environment model so as to automatically generate a set of potentially useful dynamics and convert these to action descriptions. The output action descriptions are designed to be input to an Interval Temporal Logic based planner. A series of examples of reaction construction drawn from the kitting robot domain is worked through, and the prototype implementation of the approach described.
Full citation: Damian M. Lyons and A.J. Hendriks, Exploiting patterns of interaction to achieve reactive behavior, Artificial Intelligence 73(1-2), 1995, pages 117-148.
A Situated View of Representation and Control
Stanley J. Rosenschein Teleos Research 576 Middlefield Road Palo Alto, California 94301 email@example.com Leslie Pack Kaelbling Box 1910, Computer Science Department Brown University Providence, Rhode Island 02912 firstname.lastname@example.orgIntelligent agents are systems that have a complex, ongoing interaction with an environment that is dynamic and imperfectly predictable. Agents are typically difficult to program because the correctness of a program depends on the details of how the agent is situated in its environment. In this paper, we present a methodology for the design of situated agents that is based on situated-automata theory. This approach allows designers to describe the informational content of an agent's computational states in a semantically rigorous way without requiring a commitment to conventional run-time symbolic processing. We start by outlining this situated view of representation, then show how it contributes to design methodologies for building systems that track perceptual conditions and take purposeful actions in their environments.
Full citation: Stanley J. Rosenschein and Leslie Pack Kaelbling, A situated view of representation and control, Artificial Intelligence 73(1-2), 1995, pages 149-173.
The Use of Dynamics in an Intelligent Controller for a Space Faring Rescue Robot
Marcel Schoppers Robotics Research Harvesting P.O. Box 2111 Redwood City, California 94063 email@example.comThe NASA Extra Vehicular Activity Retriever (EVAR) robot is being designed to retrieve astronauts or objects that become detached from the orbiting Space Station. This task requires that the robot's intelligent controller must rely heavily on orbital dynamics predictions, without becoming blind to the wide variety of anomalies that may occur.
This article describes the controller's Universal Plan (U.P.) and some technical lessons learned from it. The U.P. reacts not to actual current states but to estimated states, which are obtained using goal-directed active perception. A modal logic formalization of discrete-event dynamics allows us to finely analyze and specify the interactions of knowledge, belief, sensing, acting, and time within the U.P. The U.P. now acts like a hands-off manager: it makes regular observations, grants some leeway for unobservable or ill-modelled processes, has faith in subsystem dynamics, and takes action only to manipulate subsystems into delivering desired progress. Most of the time, the appropriate action is to do nothing.
Finally we examine properties of the application that allowed the U.P. to deliver robust goal achievement despite misleading state estimates, weak models of relevant processes, and unpredictable disturbances.
Full citation: Marcel Schoppers, The use of dynamics in an intelligent controller for a space faring rescue robot, Artificial Intelligence 73(1-2), 1995, pages 175-230.
On Social Laws for Artificial Agent Societies: Off-Line Design
Yoav Shoham Department of Computer Science Stanford University Stanford, California 94035 firstname.lastname@example.org Moshe Tennenholtz Faculty of Industrial Engineering and Management Technion -- Israel Institute of Technology Haifa 32000 Israel email@example.comWe are concerned with the utility of social laws in a computational environment, laws which guarantee the successful coexistence of multiple programs and programmers. In this paper we are interested in the off-line design of social laws, where we as designers must decide ahead of time on useful social laws. In the first part of this paper we suggest the use of social laws in the domain of mobile robots, and prove analytic results about the usefulness of this approach in that setting. In the second part of this paper we present a general model of social law in a computational system, and investigate some of its properties. This includes a definition of the basic computational problem involved with the design of multi-agent systems, and an investigation of the automatic synthesis of useful social laws in the framework of a model which refers explicitly to social laws.
Full citation: Yoav Shoham and Moshe Tennenholtz, On social laws for artificial agent societies: Off-line design, Artificial Intelligence 73(1-2), 1995, pages 231-252.
Instructions, Intentions and Expectations
Bonnie Webber Norman Badler Barbara Di Eugenio Chris Geib Libby Levison Michael Moore Department of Computer and Information Science University of Pennsylvania 200 South 33rd Street Philadelphia, Pennsylvania 19104-6389 firstname.lastname@example.orgBased on an ongoing attempt to integrate Natural Language instructions with human figure animation, we demonstrate that agents' understanding and use of instructions can complement what they can derive from the environment in which they act. We focus on two attitudes that contribute to agents' behavior -- their intentions and their expectations -- and shown how Natural Language instructions contribute to such attitudes in ways that complement the environment. We also show that instructions can require more than one context of interpretation and thus that agents' understanding of instructions can evolve as their activity progresses. A significant consequence is that Natural Language understanding in the context of behavior cannot simply be treated as "front end" processing, but rather must be integrated more deeply into the processes that guide an agent's behavior and respond to its perceptions.
Full citation: Bonnie Webber, Norman Badler, Barbara Di Eugenio, Chris Geib, Libby Levison, and Michael Moore, Instructions, intentions and expectations, Artificial Intelligence 73(1-2), 1995, pages 253-269.
Reinforcement Learning in Non-Markov Environments
Steven Whitehead STMS, Adaptive Systems Department GTE Laboratories Incorporated 40 Sylvan Road Waltham, Massachusetts 02254 email@example.com Long Ji Lin Siemens Cororate Research Inc 755 College Road East Princeton, New Jersey 08540 firstname.lastname@example.orgTechniques based on reinforcement learning (RL) have been used to build systems that learn to perform nontrivial sequential decision tasks. To date, most of this work has focused on learning tasks that can be described as Markov decision processes. While this formalism is useful for modeling a wide range of control problems, there are important tasks that are inherently non-Markov. We refer to these as hidden state tasks since they arise when information relevant to identifying the state of the environment is hidden (or missing) from the agent's immediate sensation. Two important types of control problems that resist Markov modeling are those in which (1) the system has a high degree of control over the information collected by its sensors (e.g., as in active vision), or (2) the system has a limited set of sensors that do not always provide adequate information about the current state of the environment. Existing RL algorithms perform unreliably on hidden state tasks.
This article examines two general approaches to extending reinforcement learning to hidden state tasks. The Consistent Representation (CR) Method unifies recent approaches such as the Lion algorithm, the G-algorithm, and CS-QL. The method is useful for learning tasks that require the agent to control its sensory inputs. However, it assumes that, by appropriate control of perception, the external states can be identified at each point in time from the immediate sensory inputs. A second, more general set of algorithms in which the agent maintains internal state over time is also considered. These stored-state algorithms, though quite different in detail, share the common feature that each derives its internal representation by combining immediate sensory inputs with internal state which is maintained over time. The relative merits of these methods are considered and conditions for their useful application are discussed.
Full citation: Steven D. Whitehead and Long-Ji Lin, Reinforcement learning of non-Markov decision processes, Artificial Intelligence 73(1-2), 1995, pages 271-306.