Agents in different types of applications

Examples of agents in different types of applications

Reactivity

In a fixed environment, like that of a compiler, programs operate predictably without the need for constant adaptation. They execute predefined tasks without concern for external changes. However, real-world environments are dynamic and unpredictable. Information evolves, circumstances shift, and new challenges emerge. Building software for such dynamic domains is complex because programs must anticipate and react to changing conditions. Unlike passive systems, reactive systems engage in ongoing interactions with their environment. They continuously monitor inputs, detect changes, and respond promptly to maintain relevance and effectiveness. This proactive responsiveness ensures that actions are timely and aligned with evolving circumstances, maximizing the system's utility and adaptability.

Proactiveness:

While reacting to environmental stimuli is straightforward, we often desire agents to go beyond mere reactions. Proactiveness entails more than just responding to events; it involves setting and pursuing goals. Proactive agents exhibit initiative by actively seeking out opportunities and taking action to achieve desired outcomes. This behavior contrasts with reactive systems governed by stimulus-response rules.

Balancing Reactive and Goal-Oriented Behavior:

The challenge lies in balancing reactive responses with goal-directed behavior. An ideal agent should not only respond promptly to immediate changes but also work systematically towards long-term objectives. However, these two objectives can sometimes conflict. Designing agents capable of effectively managing this balance remains an ongoing research endeavor. Such agents must navigate between seizing immediate opportunities and maintaining focus on overarching goals, ensuring adaptability and efficiency in dynamic environments.

Social Ability:

In the real world, environments often involve interactions between multiple agents. These agents may have their own goals and behaviors, making cooperation essential for achieving certain objectives. This holds true not only in human interactions but also in various computer environments, such as the Internet. Social ability in agents refers to their capacity to engage in communication and collaboration with other agents, and possibly even humans, using specialized communication protocols or languages. Agents with social ability can coordinate their actions, exchange information, negotiate, and cooperate towards shared or complementary goals, enhancing their effectiveness in complex, multi-agent scenarios.

Other Properties:

Several additional properties are pertinent to the concept of agency:

• Mobility: Some agents possess the capability to move within electronic networks, enabling them to access different resources or adapt to changing conditions.

• Veracity: An agent is expected to communicate information truthfully and accurately, refraining from knowingly disseminating false or misleading data.

• Benevolence: This property implies that agents prioritize cooperation and harmony, avoiding conflicts of interest with other agents. They strive to fulfill requests or obligations without intentional obstruction.

• Rationality: Rational agents act purposefully to achieve their objectives, selecting actions that are conducive to goal attainment based on available information and beliefs. They do not engage in behaviors that directly undermine their own goals.

• Learning/Adaptation: Agents have the capability to improve their performance over time through learning and adaptation. They can acquire new knowledge, refine their strategies, and adjust their behaviors in response to changing circumstances or feedback.

Agents and Objects:

While both agents and objects encapsulate state and communicate through message passing, they differ significantly in their behaviors and capabilities:

• Autonomy: Agents exhibit autonomy, making decisions and taking actions independently based on their objectives and perceptions of the environment, whereas objects typically follow predetermined instructions and lack independent decision-making abilities.

• Behavior: Agents can engage in complex, goal-directed behaviors, including proactive, reactive, and social actions, while objects primarily respond to method invocations with predefined functionalities.

• Interactivity: Agents interact with their environment and other agents dynamically, adapting their behaviors in real-time, whereas objects primarily serve as passive components within a larger system, responding to external requests.

• Purpose: Agents are designed to fulfill specific objectives or goals, often requiring flexibility and adaptability, while objects are typically used to model entities or components within a system, with a focus on encapsulating data and behaviors related to that entity.

Agents and Expert Systems:

A common question arises regarding the distinction between agents and expert systems. Expert systems traditionally encapsulate expertise in specific domains, such as medical diagnostics or financial analysis, often relying on rules and logical inference to provide advice or solutions. For instance, MYCIN is a renowned expert system specializing in diagnosing blood diseases in humans, offering recommendations based on a vast repository of rules.

However, several fundamental differences set agents apart from expert systems:

• Situation in an Environment: Agents operate within environments, actively perceiving and interacting with them, while expert systems typically remain detached from their surroundings. For instance, MYCIN lacks awareness of the world beyond the information provided to it by users.

• Action: Unlike expert systems, which primarily offer advice or recommendations, agents are capable of taking actions directly in their environment to accomplish tasks or achieve goals. In contrast, MYCIN does not directly intervene in medical procedures but instead provides diagnostic insights to human practitioners.

• Real-time Operation: Some expert systems, particularly those involved in process control, may exhibit agent-like behavior by actively influencing their environment. However, many expert systems are not designed for real-time interaction or dynamic adaptation.

Intelligent Agents and AI:

While artificial intelligence (AI) encompasses a broad spectrum of goals, including natural language understanding, scene recognition, and creative thinking, the development of intelligent agents focuses on practical problem-solving within specific domains. Although AI aims for human-like cognitive abilities, such as generalizing knowledge across diverse contexts, agents target more narrowly defined objectives, typically in constrained environments.

The distinction between AI and agents lies in their scope and objectives:

• Objective of Agent Development: Instead of striving to solve all the challenges of AI, the primary goal of building an agent is to create a system capable of selecting appropriate actions to accomplish predefined goals. This focus enables the development of practical solutions within limited domains without addressing the full breadth of AI challenges.

• Pragmatic Approach: Building useful agents does not necessitate solving the entirety of AI's complex problems. Even agents with modest intelligence can deliver valuable outcomes, as evidenced by commercial successes like NETBOT, Inc. Oren Etzioni's statement highlights the pragmatic approach of incrementally enhancing agent intelligence to achieve practical results.

Environments:

Agents operate within diverse environments, each characterized by specific attributes that influence their behavior and performance:

• Accessibility: Environments vary in accessibility, with some providing comprehensive, up-to-date information readily available to agents. However, many real-world environments, such as the physical world and the Internet, are inaccessible, posing challenges for agents to perceive and navigate effectively.

• Determinism: Environments may exhibit deterministic or non-deterministic behavior, affecting the predictability of outcomes resulting from agent actions. While deterministic environments offer certainty regarding action consequences, non-deterministic environments introduce uncertainty, complicating agent decision-making.

• Episodic Nature: Environments can be episodic or non-episodic, influencing the continuity of agent interactions. Episodic environments comprise discrete episodes with independent performance, simplifying agent decision-making within each episode without requiring consideration of future interactions.

Environments - Static vs. Dynamic:

• Static Environment: In a static environment, conditions remain unchanged unless acted upon by the agent. These environments are relatively predictable, allowing agents to anticipate outcomes of their actions accurately. For example, a chessboard remains static until a player makes a move, influencing subsequent states.

• Dynamic Environment: Conversely, a dynamic environment undergoes continuous change due to external processes beyond the agent's control. These changes can occur independently of the agent's actions and may introduce unpredictability into the environment. The physical world serves as a prime example of a highly dynamic environment, where factors such as weather, traffic, and human behavior constantly influence conditions.

Environments - Discrete vs. Continuous:

• Discrete Environment: In a discrete environment, actions and percepts occur in a fixed, finite set of possibilities. For instance, a chess game involves a discrete environment where each move and outcome is well-defined and countable. Such environments lend themselves to systematic analysis and can be theoretically handled using methods like lookup tables.

• Continuous Environment: On the other hand, a continuous environment presents an infinite or uncountable range of actions and percepts. Examples include real-world scenarios like taxi driving, where variables such as speed, direction, and traffic flow exist along continuous spectra. Continuous environments pose challenges for computer systems due to the inherent mismatch with discrete computational models.

Agents as Intentional Systems:

• Folk Psychology: Human behavior often gets explained using folk psychology, attributing actions to beliefs, desires, intentions, and emotions. For instance, one might say Janine took her umbrella because she believed it would rain, invoking beliefs and desires to explain her behavior.

Intentional Systems:

• Definition: Philosopher Daniel Dennett introduced the concept of intentional systems to describe entities whose behavior can be understood by attributing beliefs, desires, and rational reasoning to them. These intentional systems vary in complexity, from first-order systems with basic beliefs and desires to second-order systems capable of reasoning about others' mental states.

Attribution to Computer Systems:

• The question arises whether it's legitimate or useful to attribute beliefs, desires, and other mental states to computer systems. While machines lack consciousness and emotions, attributing intentional states to them can aid in understanding their behavior and structure.

Legitimacy of Attribution:

• Computer scientist John McCarthy argued that attributing mental qualities to machines is legitimate if it helps elucidate their behavior, structure, or aids in their improvement. This intentional stance simplifies understanding and programming complex systems.

Intentional Description of Objects:

• According to philosopher Yoav Shoham, almost anything, including mundane objects like light switches, can be described using the intentional stance. For example, one could view a light switch as an agent that "transmits current at will" based on its beliefs about user intentions.

Utility of Intentional Stance:

• While intentional descriptions provide a coherent framework for understanding behavior, their utility diminishes when simpler, mechanistic explanations suffice. However, with increasingly complex systems, such as modern computer architectures, intentional explanations offer valuable abstraction tools to comprehend their operations.

Abstraction Tool:

• Intentional notions serve as abstraction tools, facilitating the description, explanation, and prediction of complex systems' behavior. Like other computing abstractions, such as procedural abstraction and object-oriented programming, intentional systems offer a powerful framework for understanding agents and their interactions.

Application in Computing:

• Leveraging intentional stance as an abstraction tool in computing enables a familiar and convenient approach to describe, understand, and program complex systems. This viewpoint aligns with the broader trend in computer science towards utilizing abstraction mechanisms to manage system complexity effectively.

Advantages of Intentional Perspective:

• By characterizing agents using intentional systems, we gain a non-technical means of understanding and explaining their behavior. This approach also facilitates the specification of nested representations, essential for agents cooperating with other entities.

Post-Declarative Systems:

• Viewing agents as intentional systems leads to post-declarative programming paradigms, where systems are specified abstractly, and control mechanisms determine actions based on built-in theories of agency. This approach contrasts with procedural and declarative programming, offering a flexible framework for agent-based systems.