1. Mobile Software Agents April 18, 2001

2. Introduction: History research on agents was originated by J. McCarthy in the mid-1970’s the term agent was coined by O.G. Selfridge

3. Introduction: According to some... Agent is an intelligent robot, or “humanoid”, that has emotions, feelings and perceptions, and therefore is concerned with cognitive science, speech acts, etc.

4. Introduction: contents of this talk What are they? What can they do? Main characteristics Basic definitions Agents and DOOP Mobile agent languages Reasoning and learning techniques

5. Agents - what are they?

6. a tool (program) to perform client-server computing by transmitting running programs between clients and servers; (White 1994) Agents - what are they? server

7. Client-Server vs. Mobile Agents Client Server Client Agent Server Client Agent Server Client Agent TraditionalMobile Agent-Based

8.  find and filter information  customize views of information (e.g. email)  automate work (respond to events, such as a new version) Agents - what can they do?

9.  make recommendations and perform corporate tasks; e.g. scheduling of meetings execute diagnostics, e.g. in networks rapid (re)deployment of applications active network load balancing Agents - what can they do?

10.  distributed OO  adaptive learning systems  AI, expert systems, genetic algorithms  electronic commerce collaborative environment mobile (nomadic) computing Agents - where are they used?

11. Agents - main characteristics

12.  autonomous execution (life); have control over their own actions and may operate without the direct intervention of humans  intelligent (perform domain oriented reasoning) Agents - main characteristics

13.  perceive their environment  adaptive (they learn)  mobile (they move)  persistent (they have their own idea as to how to accomplish a task) Agents - main characteristics

14.  goal oriented (they realize a set of goals)  reactive (or reflexive); they perceive environment and timely and accurately respond to changes that occur in it active, or proactive: act to accomplish goals (take initiative not only respond to the environment) Agents - main characteristics

15. An intelligent agent has some artificial intelligence; for example based on a set of facts and inference rules (ES) learning agents are adaptive; they can learn themselves about a subject in question by statistically matching subjects of interest with particular people Intelligent Agents

16. Basic Definitions

17. An agent system can create, interpret, execute, transfer and terminate agents A host can contain several agent systems; each is uniquely identified by its name and address. Basic Definitions

18. Both, an agent and an agent system have an authority; a person or organization for whom they act An agent executes in a context, called the place; there may be one or more places within an agent system Basic Definitions

19. agents have locations (names of their current places) agents have names (the agent’s authority and identity; a unique value) Basic Definitions

20. There are two kinds of agents: stationary agent is permanently attached to a place; often resource managers, server programs or search engines mobile agent can move from one place to another

21. Agents and DOOP: DOOP distribute applications use a number of network nodes, rather than a single node communicate; for example by messaging (asynchronous)  MPI (Message Passing Interface) remote procedure call (RPC), Remote Method Invocation (RMI) (synchronous)

22. Agents and DOOP: Efficiency Which one is better, to send data to the program to send a program to the source of data (move the code closer to data)

23. Client-Server vs. Mobile Agents Client Server Client Agent Server Client Agent Server Client Agent TraditionalMobile Agent-Based

24. Agents and DOOP: Robustness Agents are better in their ability to recover from server breakdowns or unavailability (disconnected operations)

25. Agents and DOOP: Flexibility Mobile agents are particularly useful for rapid deployment of applications and dynamic updates of software; a code server can provide required code (this is also called code on demand). Agents can be used for dynamic extensions of server services.

26. Agents and DOOP: Design Designing a client/server architecture requires making all decisions about the communication between the server(s) and the client(s). These decisions are tightly coupled with the underlying problem of specific design and are very difficult or even impossible to change.

27. Mobile Agent Languages

28. An executing unit, EU is a single process (or thread) image of execution. EU is always considered in the context of a place, which contains components; either EUs, or resources, such as files. A MAL is a language designed for distributed systems, which supports EUs migrating between various places. Mobile Agent Languages: Definitions

29. An EU consists of –a static code segment –a program state: data space containing accessible resources execution state containing system information such as program counter and return address. The EU has a distributed state if its data space exists in more than one place. Mobile Agent Languages: Definitions

30. For an agent to be moved, first it has to be suspended. An entry point is a point where execution of a suspended agent is resumed Two kinds of resumption: –standard resumption (after “go”) –itinerary; one or more entry points may be explicitly specified. Mobile Agent Languages: Definitions

31. Strong mobility means that the entire code and execution state of EUs can be moved Strong mobility combined with standard resumption == strong MAL (implies that it must be possible to save the state of the execution, and later, to restore this state) Mobile Agent Languages: Definitions

32. Strong MALs: –Java with a modified JVM Weak MALs: –Aglets Mobile Agent Languages: Definitions

33. Mobile Agent Languages: Dynamic Linking

34. Name resolution must be able to bind names to both local and remote entities remote code dynamic linking: the code downloaded from a remote site and linked with an EU local resource dynamic linking: arriving EU links with resources available in this place; e.g. link with libraries. Mobile Agent Languages: Dynamic Linking

35. the standard class loader provides local resource linking and the user defined class loader can support remote code linking The programmer has a choice of –fully resolving the class, i.e. load the code closure –partially resolving the class, i.e. postpone resolving dependent classes until later time. Mobile Agent Languages: Dynamic Linking in Java

36. Mobile Agent Languages: Dynamic Linking in MALs library site provides precompiled code that can be picked up by an agent to use at other sites we consider three kinds of dynamic linking: 1.Local only dynamic linking: All the bindings are voided when a program moves.

37. Mobile Agent Languages: Dynamic Linking in MALs three kinds of dynamic linking: 2.Code with a reference (sticky): A binding is retained as long as there is at least one reference; otherwise it is garbage-collected. 3.User-specified dynamic links: Gives the programmer complete control over linking.

38. Mobile Agent Languages: Communication

39. agent to server (host); this is typically a client/ server type of interaction (e.g. based on RPC) agent to agent; this a peer-to-peer type of interaction and could be supported by messaging agent to group; this is a group communication (broadcasting) user to agent interaction; this a standard human- computer interaction, HCI. Mobile Agent Languages: Types of Communication

40. Mobile Agent Languages: So what are these languages!

41. Agent Languages: Java Knowledge Query and Manipulation Language (KQML): – is designed to support interaction among intelligent software agents. – agent’s messages, composed in a language of its own choice, wrapped inside a KQML message.

42. Agent Languages:(cont.) Agent Process Interaction Language (April): is used for building agent applications that supports: –multitasking, –network transparent message passing, –symbolic processing, and –interface to other programming languages such as "C". April is an object based language.

43. Agent Languages:(cont.) Mobile Agent System Interoperability Facility (MASIF)  Object Management Group (OMG) –a milestone on the road toward enabling location transparent interactions between static and mobile objects/agents. –to achieve interoperability between different mobile agent platforms without enforcing radical platform modifications.

44. Agent Languages:(cont.) Langage d'Agents Logiciel Objet (LALO): –is used for developing intelligent multi-agent systems using Agent Oriented Programming (AOP). –communities of agents interact by exchanging information sending specific requests, offering services, accepting or refusing tasks, competing with each other for a task to be accomplished or co-operating with each other.

45. Reasoning and Learning Techniques: Rule-Based Reasoning Knowledge-Based Reasoning Simple Statistical Analysis Fuzzy Agents Neural Networks Evolutionary Computing

46. Security

47. In general, we need to: protect agent hosts from agents agents from agent hosts one agent from another one agent host from another a group of hosts the communication between agent hosts. Security: Protection

48. Criticism

49. Sheinderman: “There is a growing danger that agents will be a deception and an empty promise.” “… machines are not people, nor can they ever become so...” “… For me, computers have no more intelligence that a wooden pencil...”

50. Questions? THANK YOU!

51. Rule-Based Reasoning Rule-base reasoning is the base for inference engine. usually in form of IF...THEN statements (production rules). LHS  Condition; RHS  Action Users can specify the rules or the agent systems can supply the rules, after training. Agents use the set of rules to decide which action or actions they should take.

52. Rule-Based Reasoning: (cont.) Multiple conditions and actions can occupy the LHS or RHS, respectively. With multiple rules, one rule’s action may cause the satisfaction of another rule’s conditions. This kind of chained effect is called forward chaining, which is widely used in expert systems.

53. Rule-Based Reasoning: (cont.) Here the agent is opportunistic. The user initiate the rules (and knowledge) and maintain the rules over time, as habits or events change. IBM’s RAISE (Reusable Agent Intelligence Software Environment) is an example of rule- based reasoning system. RAISE is the inference engine of IBM’s Agent Building Environment (ABE) developer’s toolkit.

54. Rule-Based Reasoning: (cont.) Applications for RAISE include e-commerce shopping, customer service, support, workflow on the Web and e-mail. PROBLEMS: –users must keep them up to date manually. (can not change by themselves) –complex sets of rules may develop conflicting rules that the agent can’t resolve.

55. Knowledge-Based Reasoning: One can build knowledge bases based on a specific subject area or domain. KB serves as the basis for some inference mechanisms, including the rule-based reasoning techniques. Programs are endowed with information about the task in a specific domain.

56. Knowledge-Based Reasoning: (cont.) Problems: requires a large amount of work from the knowledge engineers. The knowledge of the agent is fixed and cannot be customized to the habits of individual users. In highly personalized applications the knowledge engineer cannot possibly anticipate the best aid for each user in each situation.

57. Knowledge-Based Reasoning: (cont.) The best known example in this area is Cyc knowledge base, from Cycorp, Inc., based in Austin, Texas. Cyc is used to build Cyc agents, each with a common core of knowledge. Cyc agents communicate with each other and perform inferencing in a collaborative fashion. The inter-agent communication may use KQML (Knowledge Query and Manipulation Language). “University of Maryland”

58. Simple Statistical Analysis: Statistical Analysis is the simplest reasoning/learning technique that an intelligent agent can use. It can determine the temporal or non-temporal correlation among events of interest. Charles River Analytics’ Open Sesame and General Magic’s Magic Cap are two such examples.

59. Simple Statistical Analysis: (cont.) Open Sesame periodically scans and analyzes the logs of user actions to find repeated sequences of actions. Magic Cap recognizes frequently contacted people by their first names. EVA (evolving agent) technology uses statistical analysis to find terms that co-occur and should be added to a query.

60. Fuzzy Agents: When an agent needs to reason with: – imprecise information – incomplete information – the domain variables consist of linguistic variables (fuzzy) fuzzy logic is a useful tool.

61. Fuzzy Agents: (cont.) Fuzzy logic => expert systems. Variables can have degrees of truthfulness or falsehood represented by a range of values between 1 (true) and 0 (false). FuzzyExpert from Indigo Software can embed fuzzy logic directly into agents. FA helps to design decision support or crisis management systems that offer a range of alternative actions to solve a problem.

62. Neural Networks: Neural networks handle unstructured data or noisy data effectively. consist of a set of interconnected nodes. Each node has a weight assigned to it. Like brains, neural nets need training by experience. Training sets of data consist of two parts: the set of training data and the “right” answers extracted from that data.

63. Neural Networks: (cont.) They need training with large amounts of data in order to develop the right patterns. They can perform non-linear mappings between their input and output patterns. The most popular type is three-layer, feed- forward neural networks (input layer, output layer, and a hidden layer).

64. Neural Networks: (cont.) Each unit in a given layer connects with all the units in the neighboring layers, but not with those in the same layer. Each connection has a weight, which represents the strength of the connection. Given a set of weights, the entire network can be thought of as a mapping from a set of input vectors to a set of output vectors.

65. Neural Networks: (cont.) Inserted in an information agent: –the input vector represents a set of query terms, –the output vector indicates the “relevance” of the input vector to a certain information need. Agent systems can identify sequences of user actions, and train agents to automatically assign documents or Web pages to pre-defined categories. Autonomy’s Concept Agents or TextWise’s EVA Agents.

66. Neural Networks: (cont.) PROBLEM: Intelligent agents based on neural networks can only learn locally (their learning experiences are restricted to the documents they have scanned or the Web sites they have traveled through.).

67. Evolutionary Computing: To Expand the learning, a genetic algorithm for operating in a higher level and view things from an inter-agent perspective need to be employed. The local level of individual agents and the global level of inter-agent operations: –ensure the optimization of each agent from local knowledge –expend agents learning based on global knowledge.

68. Evolutionary Computing: (cont.) The goal is to “breed” a new generation of agents that benefit from the learning experiences of individual “parent”. uses biologically based evolutionary processes as a model for implementation. The most popular examples of evolutionary computing are genetic algorithms.

69. Evolutionary Computing: (cont.) They work by maintaining a population of possible solutions (chromosomes, or agents in our case). Continues evaluations of the performance of the agents determine: – unfit set of agents to be terminated, – fittest set of agents to be recombined to produce possibly better agents. TextWise is developing such a technology under a contract from the National Imagery and Mapping Agency (NIMA).

70. Security

71. a security policy is a set of guidelines describing whether various actions are allowed or not (may also include royalties) a security policy is static if its guidelines do not depend on external conditions; otherwise it is dynamic. a security policy may include credentials, or level of trust. Security: Definitions

72. Java Security Manager implements a static security policy, which controls access to resources such as file I/O, network access, and others. There is no provision to limit access to other resources such as CPU cycles. Security: Definitions

73. In general, we need to: protect agent hosts from agents agents from agent hosts one agent from another one agent host from another a group of hosts the communication between agent hosts. Security: Protection

74. Doable, but two agents operating on the same host share the same interpreter and they share memory (no hardware faults) Software Fault Isolation inserts a checking code before each unsafe instruction Sandboxing before each unsafe instruction inserts a code which sets the high order bits of the target address to the correct segment identifier Security: Protecting one agent from another

75. cryptography can be used to authenticate credentials a credential means that the receiver will trust the sender access-level monitoring and control uses a security manager which maintains a list of allowed activities (perhaps associated with fees) - can be misleading Security: Protecting agent hosts from agents

76. code verification by the host may try to examine the code of the agent (does not help if the agent is self-modifiable) the host can apply various limitation techniques, e.g. limit the time, or record all agent activities. Security: Protecting agent hosts from agents

77. A static byte code verifier checks for the right magic number in the class file, and performs data flow analysis on each method to test for things such as branches must be within the bounds of the code, or there is no attempt to access variables which are not in the scope JVM performs various run-time checks. Security: Protecting agent hosts from agents

78. Trail obscuring means that the agent constantly modifies its image so it can avoid tracing by hosts Code obfuscation means that the agent is sent together with a kind of interpreter, so the host doesn’t really see the code of this agent. Security: Protecting agent hosts from agents

79. a host can steal information (normal routing) a host can mutate an agent (e.g. rerouting) since the host agent has access to an agent, it is not possible to protect an agent from the agent host (unless a specialized hardware is used) Security: Protecting agent from agents hosts

80. visit only trusted sites and from there use safer mechanisms, such as RPC or stateless agents maintain reliable data that can be used (after the fact) to determine whether the agent has been tampered with divide an agent into components, encrypted each component (when traveling through untrusted sites); update only while on trusted sites Security: Protecting agent from agents hosts

81. maintain a safe migration history and use it against rerouting attacks; or keep audit logs there is new research on encrypting programs which are equivalent to the original programs, are directly executable and produce encrypted output. Security: Protecting agent from agents hosts

82. Network Awareness

83. Network Awareness: Requirements awareness - an ability to monitor resources agility - an ability to react to changes in resources authority - an ability to control the way resources are used on their behalf by support code.

84. Network Awareness: Requirements Resource awareness can be represented by: on-demand monitoring continuous monitoring (specific filters should be used to avoid jitters).

85. Network Awareness: Requirements Authority can give a complete control over resources, with explicit authorization for every use (like in applets) consider a resource violation as an asynchronous event and associate a handler with every restriction on resource.

86. “...asking the question of what an agent is to a DAI researcher is as embarrassing as the question of what intelligence is for an AI researcher...” Carl Hewitt Introduction:DAI