Agent-Based Simulation of Animal Behaviour

In the biological literature on animal behaviour, in addition to real experiments and field studies, also simulation experiments are a useful source of progress. Often specific mathematical modelling techniques are adopted and directly implemented in a programming language. Modelling more complex agent behaviours is less adequate using the usually adopted mathematical modelling techniques. The literature on AI and Agent Technology offers more specific methods to design and implement (also more complex) intelligent agents and agent societies on a conceptual level. One of these methods is the compositional multi-agent system design method DESIRE. In this paper it is shown how (depending on the complexity of the required behaviour) a simulation model for animal behaviour can be designed at a conceptual level in an agent-based manner. Different models are shown for different types of behaviour, varying from purely reactive behaviour to pro-active, social and adaptive behaviour. The compositional design method for multi-agent systems DESIRE and its software environment supports the conceptual and detailed design, and execution of these models. A number of experiments reported in the literature on animal behaviour have been simulated for different agent models.     

A compositional process control model and its application to biochemical processes

A compositional generic process control model is presented which has been applied to control enzymatic biochemical processes. The model has been designed at a conceptual and formal level using the compositional development method DESIRE, and includes processes for analysis, planning and simulation. It integrates qualitative and quantitative techniques. Its application to enzymatic chemical processes is described.     

Multi-agent role allocation: issues,approaches, and multiple perspectives

In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multi-agent systems. Along with a discussion of these issues about roles in multi-agent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multi-agent systems.     

On generalization/specialization for conceptual graphs

Abstract

This paper centres on the generalization/specialization relation in the framework of conceptual graphs (this relation corresponds to logical subsumption when considering logical formulas associated with conceptual graphs). Results given here apply more generally to any model where knowledge is described by labelled graphs and reasoning is based on graph subsumption, as in semantic networks or in structural machine learning. The generalization/specialization relation, as defined by Sowa, is first precisely analysed, in particular its links with a graph morphism, called projection. Besides Sowa's specialization relation (which is a preorder), another one is actually used in some practical applications (which is an order). These are comparatively studied. The second topic of this paper is the design of efficient algorithms for computing these specialization relations. Since the associated problems are NP-hard, the form of the graphs is restricted in order to arrive at polynomial algorithms. In particular, polynomial algorithms are presented for computing a projection from a conceptual ‘tree’ to any conceptual graph, and for counting the number of such projections. The algorithms are also described in a generic way, replacing the projection by a parametrized graph morphism, and conceptual graphs by directed labelled graphs.     

Avatars of the tortoise: life,longevity and simulation

Abstract

We present a new way of dynamically growing and breeding structures in 3D space through swarming agents. Different agent types and the way they evolve over time is specified by a swarm grammar similar to Lindenmayer systems. We expand common L-system string interpretation from a single turtle to a multitude of turtles which behave like a swarm. By describing swarm agents within the framework of formal grammars, we build a bridge from symbolic production systems (rewrite systems) to three-dimensional real-time construction procedures that are executed by reactive and interacting agents which move in simulated physical 3D spaces. We introduce constructor agents, their formal representation in swarm grammars and demonstrate by examples how (1) the swarm rules, (2) the agent parameters and (3) the environ ment can influence the actual construction and growth processes that are initiated and directed by the swarms. In order to facilitate exploration of a large variety of swarm grammars, we apply interactive evolutionary design methods to create swarm grammar sculptures and 3D structures.     

Modelling a Society of Simple Agents: From Conceptual Specification to Experimentation

Much research concerning the design of multi-agent systems (at a conceptual level) addresses complex agents that exhibit complex interaction patterns. Due to this complexity, it is difficult to perform rigorous experimentation. On the other hand, systematic experimental work regarding behaviour of societies of more simple agents, while reporting valuable results, often lacks conceptual specification of the system under consideration. In this paper, the compositional multi-agent modelling framework DESIRE is not only successfully used to develop a conceptual specification of the simple agents discussed by A. Cesta, M. Miceli and P. Rizzo (Lecture Notes in Artificial Intelligence, vol. 1038, Springer-Verlag: Berlin, pp. 128–138, 1996), but also to simulate the behaviour in a dynamical environment. In the DESIRE framework, a conceptual specification, which provides a high-level view of an agent, has enough detail for automatic prototype generation. The prototype implementation of the con-ceptual specification of the simple agents has been used to replicate, and extend, one of the experiments reported by Cesta et al.     

Toward a well-founded theory for multi-level conceptual modeling

Multi-level conceptual modeling addresses the representation of subject domains dealing explicitly with multiple classification levels. Despite the recent advances in multi-level modeling techniques, we believe that the literature in multi-level conceptual modeling would benefit from a theory that: (1) formally characterizes the nature of classification levels and (2) precisely defines the structural relations that may occur between elements of different classification levels. This work aims to fill this gap by proposing an axiomatic theory that can be considered a reference top-level ontology for types in multi-level conceptual modeling. The theory provides the modeler with basic concepts and patterns to articulate domains that require multiple levels of classification as well as to inform the development of well-founded languages for multi-level conceptual modeling. The whole theory is founded on a basic instantiation relation and characterizes the concepts of individuals and types, with types organized in levels related by instantiation. Further, it includes intra-level structural relations that are used to define expressive multi-level models and cross-level relations that allow us to account for and incorporate the different notions of power type in the literature.     

A language-independent and formal approach to pattern-based modelling with support for composition and analysis

ContextPatterns are used in different disciplines as a way to record expert knowledge for problem solving in specific areas. Their systematic use in Software Engineering promotes quality, standardization, reusability and maintainability of software artefacts. The full realisation of their power is however hindered by the lack of a standard formalization of the notion of pattern.ObjectiveOur goal is to provide a language-independent formalization of the notion of pattern, so that it allows its application to different modelling languages and tools, as well as generic methods to enable pattern discovery, instantiation, composition, and conflict analysis.MethodFor this purpose, we present a new visual and formal, language-independent approach to the specification of patterns. The approach is formulated in a general way, based on graphs and category theory, and allows the specification of patterns in terms of (nested) variable submodels, constraints on their allowed variance, and inter-pattern synchronization across several diagrams (e.g. class and sequence diagrams for UML design patterns).ResultsWe provide a formal notion of pattern satisfaction by models and propose mechanisms to suggest model transformations so that models become consistent with the patterns. We define methods for pattern composition, and conflict analysis. We illustrate our proposal on UML design patterns, and discuss its generality and applicability on different types of patterns, e.g. workflow patterns, enterprise integration patterns and interaction patterns.ConclusionThe approach has proven to be powerful enough to formalize patterns from different domains, providing methods to analyse conflicts and dependencies that usually are expressed only in textual form. Its language independence makes it suitable for integration in meta-modelling tools and for use in Model-Driven Engineering.     

Instrument semiotics: a semiotic approach to interface components

This paper will briefly present a semiotic approach to instrument interfaces based on a conceptual analysis of display and control components and their compositional semantics. Display and control components can be considered as prototypical objects that are themselves constructed from combinations of more elementary signs expressed in some combination of media. Different types of signs (or ‘representational modalities’) and different types of media have different semantic and syntactic properties. These properties will to some extent determine what different combinations of media and signs are good for, i.e. what kinds of information can be adequately expressed in a given media–modality combination, and what kinds of cognitive support it will give to the social agents using it in a work context. The relevance of such an approach is partly in enhancing our understanding of instrument interfaces and human–machine interaction in complex work domains and partly to support the design and development of flexible and tailorable instrument interfaces.     

Automated Assistants for Analyzing Team Behaviors

Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. The complex interactions of agents in a team as well as with other agents make it extremely difficult for human developers to understand and analyze agent-team behavior. It has thus become increasingly important to develop tools that can help humans analyze, evaluate, and understand team behaviors. However, the problem of automated team analysis is largely unaddressed in previous work. In this article, we identify several key constraints faced by team analysts. Most fundamentally, multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. In addition, effective ways of presenting the analysis to humans is critical and the presentation techniques depend on the model being presented. Finally, analysis should be independent of underlying team architecture and implementation.We also demonstrate an approach to addressing these constraints by building an automated team analyst called ISAAC for post-hoc, off-line agent-team analysis. ISAAC acquires multiple, heterogeneous team models via machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC employs multiple presentation techniques that can aid human understanding of the analyses. ISAAC also provides feedback on team improvement in two novel ways: (i) It supports principled what-if reasoning about possible agent improvements; (ii) It allows the user to compare different teams based on their patterns of interactions. This paper presents ISAAC's general conceptual framework, motivating its design, as well as its concrete application in two domains: (i) RoboCup Soccer; (ii) software agent teams participating in a simulated evacuation scenario. In the RoboCup domain, ISAAC was used prior to and during the RoboCup '99 tournament, and was awarded the RoboCup Scientific Challenge Award. In the evacuation domain, ISAAC was used to analyze patterns of message exchanges among software agents, illustrating the generality of ISAAC's techniques. We present detailed algorithms and experimental results from ISAAC's application.     

THE EXTENDED PROCESS MODEL—TRANSFORMING PROCESS SPECIFICATIONS INTO ONTOLOGICAL REPRESENTATIONS

ABSTRACT

Knowledge-intensive processes are often open ended and can be only weakly specified in traditional business process models. The extended process model provides a comprehensive integration of process and knowledge specific aspects within a single, ontological representation. In this article we describe the transformation of standardized business process definitions into a unified conceptual process model. We then apply our methodology to the scenario of IT services.     

From roles to teamwork: A framework and architecture

Teamwork requires organization, strategies, and coordination. The design of a multiagent system should support these conceptual properties for constructing effective teams. The advantage of a teamwork approach is the reduction in complexity of the task through distribution of responsibilities, resulting in better utilization of resources, robust behaviors, and a greater variety of behaviors against competitors. In this article a framework for building teams of responsible agents using roles, responsibilities, and strategies is described. Its application to the domain of soccer is used to design a high-performance team of soccer agents. The architecture for these agents utilizes a reactive planning system with support for teamwork. The team of soccer agents will be tested in a series of competitions against other teams in the real-time soccer simulator proposed for Robocup-97, which provides an uncertain, resource bounded world.     

CRAFTING THE MIND OF PROSOCS AGENTS

PROSOCS agents are software agents that are built according to the KGP model of agency. KGP is used as a model for the mind of the agent, so that the agent can act autonomously using a collection of logic theories, providing the mind's reasoning functionalities. The behavior of the agent is controlled by a cycle theory that specifies the agent's preferred patterns of operation. The implementation of the mind's generic functionality in PROSOCS is worked out in such a way so it can be instantiated by the platform for different agents across applications. In this context, the development of a concrete example illustrates how an agent developer might program the generic functionality of the mind for a simple application.     

Feedback-directed specialization of code

Based on feedback information, a large number of optimizations can be performed by the compiler. This information actually indicates the changing behavior of the applications and can be used to specialize code accordingly.Code specialization is a way to facilitate the compiler to perform optimizations by providing the information regarding variables in the code. It is however difficult to select the variables which maximize the benefit of specialization. Also the overhead of specialization and code size increase are the main issues while specializing code.This paper suggests a novel method for improving the performance using specialization based on feedback information and analysis. The code is iteratively specialized after selecting candidate variables by using a heuristic, followed by generation of optimized templates. These templates require a limited set of instructions to be specialized at runtime and are valid for a large number of values. The overhead of runtime specialization is further minimized through optimal software cache of template clones whose instantiation can be performed at static compile time.The experiments have been performed on Itanium-II(IA-64) and Pentium-IV processors using icc and gcc compilers. A significant improvement in terms of execution speed and reduction of code size has been achieved for SPEC and FFTW benchmarks.     

The Design of the Zinc Modelling Language

Zinc is a new modelling language developed as part of the G12 project. It has four important characteristics. First, Zinc allows specification of models using a natural mathematical-like notation. To do so it supports overloaded functions and predicates and automatic coercion and provides arithmetic, finite domain and set constraints. Second, while Zinc is a relatively simple and small language, it can be readily extended to different application areas by means of powerful language constructs such as user-defined predicates and functions and constrained types. Third, Zinc provides sophisticated type and instantiation checking which allows early detection of errors in models. Finally, perhaps the main novelty in Zinc is that it is designed to support a modelling methodology in which the same conceptual model can be automatically mapped into different design models, thus allowing modellers to easily “plug and play” with different solving techniques and so choose the most appropriate for that problem. We describe in detail the various language features of Zinc and the many trade-offs we faced in its design.     

Learn sesame a learning agent engine

Open Sesame! 1.0-released in 1993-was the world's first commercial user interface learning agent. The development of this agent involved a number of decisions about basic design issues that had not been previously addressed, including the expected types of agents and the preferred form and frequency of interaction. In the 2 years after shipping Open Sesame! 1.0, we have compiled a rich database of customer feedback. Many of our design choices have been validated by the general approval of our customers, while some were not received as favorably. Thanks to the overwhelming amount of feedback, we were able to substantially improve the design for Open Sesame! 2.0 and develop a cross-platform learning engine-Learn Sesame-that can be used to add learning agent functionality to any third-party application. In this article, we present a summary of the lessons learned from customer feedback, an outline of resulting design changes, the details of the developed learning agent engine, and planned research.     

Can a rational agent afford to be affectless? a formal approach

In this article, we expose some of the issues raised by the critics of the neoclassical approach to rational agent modeling and we propose a formal approach for the design of artificial rational agents that includes some of the functions of emotions found in the human system. We suggest that emotions and rationality are closely linked in the human mind (and in the body, for that matter) and, therefore, need to be included in architectures for designing rational artificial agents, whether these agents are to interact with humans, to model humans' behaviors and actions, or both. We describe an Affective Knowledge Representation (AKR) scheme to represent emotion schemata, which we developed to guide the design of a variety of socially intelligent artificial agents. Our approach focuses on the notion of "social expertise" of socially intelligent agents in terms of their external behavior and internal motivational goal-based abilities. AKR, which uses probabilistic frames, is derived from combining multiple emotion theories into a hierarchical model of affective phenomena useful for artificial agent design. AKR includes a taxonomy of affect, mood, emotion, and personality, and a framework for emotional state dynamics using probabilistic Markov Models.