Pitfalls of Agent Projects

1
Pitfalls of Agent Projects

• Borrowed from Nick Jennings
• University of Southampton, UK

2
Pragmatics of Agent-Oriented Developments

• Lots of (single and multi-) agent projects
• But pragmatics of agent-oriented development
  received little attention.
• Here identify number of key pitfalls.
• political
• management
• conceptual
• analysis and design
• micro (agent) level
• macro (society) level

Jennings and Wooldridge
3
You Oversell Agents
Political Pitfalls

• Agents are not magic
• If you cant do it with ordinary software,
  probably cant do it with agents.
• No evidence that any system developed using agent
  technology could not have been built using
  non-agent techniques.
• Agents may make it easier to solve certain
  classes of problem
• but they do not make the impossible possible.
• Agents are not AI by a back door.
• Agents have not solved all the problems that have
  dogged AI since its inception

4
Get Dogmatic about Agents
Political Pitfalls

• Agents used in wide range of applications, but
  they are not a universal solution.
• For many applications, conventional software
  paradigms (e.g., OO) are more appropriate.
• Given a problem for which an agent and a
  non-agent approach appear equally good, prefer
  non-agent solution!
• Other form of dogma
• believing in your agent definition
• and shoe-horning solution to fit this

5
Dont Know Why You Want Agents
Management Pitfalls

• Agents new technology lots of hype!
• Agents will generate US2.6 billion in
  revenue by the year 2000
• Managerial reaction we can get 10 of that
• Managers propose agent projects without having
  clear idea idea about what having agents will
  buy them.
• No business plan for the project
• pure research? ? technology vendor? ? solutions
  vendor?
• Often, projects appear to be going well. (We
  have agents!) But no vision about where to go
  with them.
• understand your reasons for attempting agent
  development project, and what you expect to gain
  from it

6
Want Generic Solutions to 1-Off Problems
Management Pitfalls

• Devising a generic architecture/testbed, when
  really need a bespoke system.
• Re-use is difficult to attain unless development
  is undertaken for a close knit range of problems
  with similar characteristics.
• General solutions are more difficult and more
  costly to develop
• often need extensive tailoring to target
  application

7
Believe Agents Silver Bullet
Conceptual Pitfalls

• Holy grail of software engineering is a silver
  bullet
• order of magnitude improvement in software
  development.
• Many technologies promoted as silver bullet
• COBOL
• automatic programming
• expert systems
• graphical programming
• Agent technology is not a silver bullet.
• Good reasons to believe that agents are a useful
  way of tackling some problems.
• But these arguments largely untested in practice.

8
Forget Agents are Software
Conceptual Pitfalls

• Agent system development is essentially
  experimentation
• No tried and trusted techniques (at present)
• Encourages developers to forget developing
  software
• Project plans focus on the agenty bits.
• Mundane software engineering (requirements
  analysis, specification, design, verification,
  testing) is forgotten.
• Result a foregone conclusion
• project flounders, not because agent problems,
  but because basic software engineering ignored.
• any principled software development technique is
  better than none.

9
Forget Agents are Multi-Threaded Software
Conceptual Pitfalls

• Multi-threaded software one of most complex
  classes of computer system to design and
  implement.
• Significant background experience in distributed
  and concurrent computing areas
• Multi-agent system tend to be multi-threaded
• both within and between agents
• need to recognise and plan for things such as
• synchronisation
• mutual exclusion for shared resources
• deadlock

10
Dont Ignore Related Technology
Analysis and Design Pitfalls

• Percentage of system that is agent-specific is
  comparatively small.
• intelligent agents are 99 computer science and
  1 AI
• (Etzioni,96)
• Important conventional technologies and
  techniques are exploited wherever possible.
• Dont reinvent the wheel.
• CORBA
• Database technology
• Expert system shells

11
Dont Exploit Concurrency
Analysis and Design Pitfalls

• One of most obvious features of a poor
  multi-agent design is that amount of concurrent
  problem solving is small.
• Serial processing in distributed system
• Only ever a single thread of control
• concurrency, one of the most important potential
  advantages of multi-agent solutions not exploited.

12
You ignore legacy
Analysis and Design Pitfalls

• When building systems using new technology, often
  an assumption that it is necessary to start from
  a blank slate.
• However in many cases, most important components
  of a software system will be legacy
• functionally essential, but technologically
  obsolete software components, which cannot
  readily be rebuilt.
• When proposing a new software solution, essential
  to work with such components.
• They need to be incorporated into an agent layer.

13
Want Your Own Architecture
Agent Level Pitfalls

• Architecture design for building agents.
• Many have been proposed over the years.
• Great temptation to imagine you need your own
• not designed here mindset
• intellectual property.
• Problems
• architecture development takes years
• no clear payback.
• Recommendation buy one, take one off the shelf,
  or do without.

14
Use Too Much AI
Agent Level Pitfalls

• Temptation to focus on intelligent aspects of
  the application.
• an agent framework too overburdened with
  experimental AI techniques to be usable.
• fuelled by feature envy
• Resist temptation to believe such features are
  essential in your system
• build agents with a minimum of AI
• success is obtained with such systems,
  progressively evolve them into richer systems.

15
No AI
Micro (Agent) Level Pitfalls

• Dont call your on-off switch an agent!
• Be realistic
• find everyday distributed systems referred to as
  multi-agent systems.
• Web pages with any behind the scenes processing
  as agents.
• Problems
• lead to the term agent losing any meaning
• raises expectations of software recipients
• leads to cynicism on the part of software
  developers.

16
See Agents Everywhere
Macro (Society) Level Pitfalls

• Pure A-O system everything is an agent!
• agents for addition, subtraction,
• Naively viewing everything as an agent is
  inappropriate.
• choose the right grain size.
• more than 10 agents big system.

17
Too Few Agents
Macro (Society) Level Pitfalls

• While some designers imagine a separate agent for
  every possible task.
• Others dont recognise value of a multi-agent
  approach at all.
• Create system with very small number of agents
  doing all the work
• fails software engineering test of coherence.
• result is like OO program with 1 class.

18
Implementing Infrastructure
Macro (Society) Level Pitfalls

• Presently, no widely-used software platforms for
  developing agents
• Such platforms provide basic infrastructure
  required to create a multi-agent system.
• The result everyone builds their own.
• By the time this is developed, project resources
  gone!
• No effort devoted to agent-specifics.

19
Agents Interact too Freely
Macro (Society) Level Pitfalls

• Numerous systems interacting with one another can
  generate behaviour more complex than sum of parts
• good exploit this emergent functionality to
  provide simple, robust cooperative behaviour
• bad emergent behaviour akin to chaos
• restrict way agents interact
• simplest possible protocol for achieving set
  objective

20
System Lacks Structure
Macro (Society) Level Pitfalls

• Common misconception is that agent systems
  require no real structuring
• throw together agents and see what happens!
• While this may be true in some cases,
• in majority of situations, considerable amount of
  system-level engineering takes place
• especially for large scale systems or where need
  some commonality of purpose
• structure helps
• reduce systems complexity
• increase efficiency
• more accurately model problem at hand

21
Conclusions

• Agent technology is immature and largely
  untested.
• Agent system developers often fall into the same
  traps.
• Described what we perceive to be most common and
  most serious of these pitfalls.
• Thereby shift attention to pragmatics of agent
  system engineering.

22
Further Reading

• N. R. Jennings, P. Faratin, A. R. Lomuscio, S.
  Parsons, C. Sierra and M. Wooldridge (2001)
  Automated negotiation prospects, methods and
  challenges Int. J. of Group Decision and
  Negotiation 10 (2).
• F. Zambonelli, N. R. Jennings, and M. Wooldridge
  (2001) "Organisational rules as an abstraction
  for the analysis and design of multi-agent
  systems" Int J. of Software Engineering and
  Knowledge Engineering.
• N. R. Jennings, P. Faratin, T. J. Norman, P.
  O'Brien and B. Odgers (2000) Autonomous agents
  for business process management Int. Journal of
  Applied Artificial Intelligence 14 (2) 145-189.
• N. R. Jennings, P. Faratin, T. J. Norman, P.
  O'Brien, B. Odgers and J. L. Alty (2000)
  Implementing a business process management
  system using ADEPT A Real-World Case Study Int.
  Journal of Applied AI 14 (5) 421-465.
• N. Vulkan and N. R. Jennings (2000) Efficient
  mechanisms for the supply of services in
  multi-agent environments Int Journal of Decision
  Support Systems 28(1-2) 5-19.
• M. Wooldridge, N. R. Jennings, and D. Kinny
  (2000) The Gaia methodology for agent-oriented
  analysis and design Journal of Autonomous Agents
  and Multi-Agent Systems 3 (3) 285-312.
• M. J. Wooldridge and N. R. Jennings, (1999)
  Software engineering with agents pitfalls and
  pratfalls IEEE Internet Computing 3 (3) 20-27.