Done by Fazlun Satya Saradhi

INTRODUCTION The main concept is to use different types of agent models which would help create a better dynamic and adaptive environment in response to the user’s actions. This is done by experimenting with three different agent models. This paper introduces a schema for characterizing the implementation and behavioral complexity of agent models for dynamic virtual environments, so as to identify the advantages and disadvantages and to identify directions for future work.

INTRO( contd…..) The paper presents three different agent models: Swarm Agent Model Cognitive Agent Model Motivated Agent Model. These models are differentiated using the following terms: Behavioral complexity Implementation complexity

Behavioural Complexity Behavioural complexity measures the richness of the reasoning process that produces the dynamics of a virtual environment. Behavioural complexity can be measured in five modes: reflexive, reactive, reflective, autonomous and proactive. Reflexive behaviour is a pre-programmed response to the state of the environment – a reflex without reasoning. Reactive behaviour manifests itself as reasoning about responses within a fixed set of goals.

Behavioural Complexity(contd..) Reflective behaviour does not simply react but hypothesises possible desired states of the environment and proposes alternate actions that will achieve those states. Autonomous behaviour does not simply select goals from a fixed set but includes reasoning processes to create new goals in response to new situations. Pro-active behaviour goes beyond reasoning about goals to be achieved and hypothesises possible undesirable future states of the environment and proposes alternate actions to avoid those states.

Implementation Complexity Implementation complexity is calculated using the amount of effort that goes into programming to produce a certain level of behavioural complexity. This depends on two properties: Domain Dependence Social Ability

Implementation Complexity( contd……) Domain Dependence: Domain specific systems are those which are tied down to a particular environment. Domain independent systems are those which are relevant to a wide range of environments. Social Ability: No communication means that every script or agent functions independently. They do not communicate. Indirect communication means that they communicate using direct stigmergy or continuous stigmergy

Implementation Complexity( contd ……) Direct communication means that they communicate directly by using messages that are sent from one script or agent to another script or agent.

Implementation Complexity( contd ……)

Agent Models An agent is a system that perceives its environment through sensors, reasons about its sensory input using some characteristic reasoning process and acts upon the environment through effectors. Agent models describe ways to implement the characteristic reasoning process of one or more agents. A diagrammatic representation of the general agent model is shown below:

The general agent model.

The Swarm Agent Model In this model the agents interact with the environment to cause coherent functional global patterns to emerge. In the swarm based model discussed in the paper each agent has two internal reasoning sub-processes, sensation and action. These processes are facilitated by three structure, sensors, memory and effectors.

Diagrammatic representation.

The Swarm Agent Model(contd…..) The sensors sense the raw data and transform it into sense data structures which are appropriate for reasoning. The action process then uses a reflexive mode to respond to sense-data by triggering effectors to make changes to the environment.

Example of the swarm agent model.

Cognitive Agent Model The cognitive model includes processes for reasoning about the world at different levels of abstraction and a direct communication structure. In this model the agents have the ability to interpret their environment based on which they set their goals. Interpretations are constructed using five successive reasoning sub-processes, sensation, perception, conception, hypothesizing and action. These processes are facilitated by three structures, sensors, memory and effectors.

Diagrammatic representation.

Cognitive Agent Model(contd ……) The sensors transform the raw data into sense data structures which are used by the perception, conception and hypothesizing processes to build more abstract interpretations of the environment. The action process reasons about sense-data, percepts, concepts and goals and selects a behavioral mode corresponding to the complexity of the current interpretation.

Example of the cognitive agent model.

INTERACTION DIAGRAM

Cognitive Agent Model(contd…...) Communication within the cognitive agent society uses the Contract Net negotiation protocol, a form of direct communication, in contrast to the indirect communication used by swarm agents.

Motivated Agent Model A motivated agent model has the potential to achieve behavioural complexity without the need for domain specific rules. Motivation is that which gives purpose and direction to behavior and motivation is the drive that arouses an organism to action towards a desired goal. The primary reasoning components of this model, are sensation, motivation, learning and action.

Diagrammatic representation.

Motivated Agent Model(contd ………) Sensors sense the raw data and convert them into sense data structures. allow motivated agents to reason about both the changes in their environment and the static states they sense. The motivation process identifies interesting sense data and events using the domain independent intuition that rare occurrences are interesting.

Example of a Motivated Agent Model

Concluding Remarks The author concludes that though these agent models can increase the behavioural complexity of the virtual environments they are limited by technical resources like speed, robustness and richness in scripting languages.

Differences in agent models Few agent models can provide better behavioural complexity than others. These differences are shown by using a detailed classification schema.

DETAILED CLASSIFICATION SCHEMA

Concluding remarks(contd…..) The swarm model gives us the benefit of applying itself to a number of a objects in a domain independent environment. The cognitive model includes processes that identify patterns and concepts from raw sensor data and use these to determine reactive and reflective behavior. The motivated agent model does not specify enough knowledge in the perception, conception and hypothesizer parts of the model to identify domain specific patterns and goals, it uses the idea of motivation to allow agents to develop their own goals and behavioural patterns according to their experience in the world. Agents in this model are unpredictable.

Future directions of work A solution to this problem is by the use of pro-active behavioural mode. Pro-active agents do not merely react to new situations or hypothesis desirable future situations, rather they can anticipate future undesirable situations and act in advance to avoid them. The ability to anticipate the needs of human controlled avatars is a step towards intelligent environments and thus a possible focus for future research.

CRITIQUE  The author has discussed a great deal about the agent models that can be used to create a dynamic and interactive virtual environment, but he has stated that none of these could be utilized to the extent needed in any of the virtual worlds be it Active Worlds SDK or SL because of limited robustness, less speed and lack of rich scripting tools. If the main theme was to implement these models in virtual worlds he should have given us a better idea regarding how these issues are dealt with.

CRITIQUE( contd ………)  The author stated that building these models over the whole of the virtual world is a issue in terms of programming the whole environment, its objects, avatars etc. He did not discuss how this issue will be solved.  It is given that in reactive mode perception of agents is a consequence of experience but it is not discussed any further. Experience can be a set of past choices made when in same situation and it can be drawn as a probability distribution curve.

CRITIQUE(contd ………)  In motivated agent model, it is given that it chooses rarely occurring events as interesting events. But it is not given that how it recognizes an event as rarely occurring or frequently occurring. There is a way in which it could happen- by storing all the previously occurred events in its memory which is not discussed.  The three models discussed are not compared by taking a single virtual world so that we can know which model works effectively than others.

CRITICAL QUESTIONS On what basis is the pre-programmed look up table for micro rules set up in the swarm agent model? How does the motivation process prioritize goals so that it acts on the most important goal? In continuous stigmergy, agents communicate by depositing pheromones but how will they communicate in discrete stigmergy?

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