A legal knowledge based system called JUSTICE is presented which can identify heterogeneous representations of concepts across all major Australian jurisdictions, and some concepts within US and UK cases. The knowledge representation scheme used for legal and common sense concepts is inspired by human processes for the identification of concepts and the expected order and location of concepts. These are supported by flexible search functions and various string utilities. JUSTICE is a client-based legal software agent which works with both plaintext and HTML representations of legal cases over file systems, and the World Wide Web. In creating JUSTICE an ontology for legal cases was developed, and this is implicit within JUSTICE.

There is a big difference between the attention artificial intelligence (AI) is currently receiving and that of the 1990s. Twenty years ago, the focus was on logic-based AI, usually under the heading of knowledge representation, or KR, whereas today's focus is on machine learning and statistical algorithms. This shift has served AI well, since machine learning and stats provide effective algorithmic solutions to certain kinds of problems (such as image recognition), in a way that KR never did. However, I contend the pendulum has swung too far, and something valuable has been lost. Knowledge representation is not a single thing.

Many and long were the conversations between Lord Byron and Shelley to which I was a devout and silent listener. During one of these, various philosophical doctrines were discussed, and among others the nature of the principle of life, and whether there was any probability of its ever being discovered and communicated. They talked of the experiments of Dr. Darwin (I speak not of what the doctor really did or said that he did, but, as more to my purpose, of what was then spoken of as having been done by him), who preserved a piece of vermicelli in a glass case till by some extraordinary means it began to move with a voluntary motion. Not thus, after all, would life be given. Perhaps a corpse would be reanimated; galvanism had given token of such things: perhaps the component parts of a creature might be manufactured, brought together, and endued with vital warmth (Butler 1998).

In 1976, Newell and Simon [Newell and Simon1976] proposed that intelligent behavior arises from the manipulation of symbols--entities that represent other entities, and that the process by which intelligence arises is heuristic search. Search is a process of formulating and examining alternatives. It starts with an initial state, a set of candidate actions, and criteria for identifying the goal state. It is often guided by heuristics, or rules of thumb,'' which are generally useful, but not guaranteed to make the best choices. Starting from the initial state, the search process selects actions to transform that state into new states, which themselves are transformed into more new states, until a goal state is generated.

You may skip most of these readings if you have prior programming experience in Java.

The following is a special contribution to this blog from Henry Kautz, Chair of the Department of Computer Science at the University of Rochester. His research interests are in knowledge representation, satisfiability testing, pervasive computing, and assistive technology. He is currently President of the Association for the Advancement of Artificial Intelligence (AAAI). If you have comments on this essay, e-mail Henry or add an entry to the bottom of this blog post. Countless gallons of ink (real and virtual) have been spilled on the need to infuse the humanities into science and engineering education.

Gister supports the rapid development of evidential reasoning systems through an interactive, menu-driven, graphical interface, based upon Grasper-CL. The user interacts with the system in much the same way as with electronic spreadsheets, by simply selecting from menus to add evidential operations to an analysis, to modify data or operation parameters, or to change any portion of the uncertain knowledge base. In response, gister updates its analyses to reflect the new information. Gister supports a wide range of evidential operations, including fusion, source discounting, time projection, summarization, evidence interpretation, and sensitivity analysis. Gister has been applied to a wide range of problems, including multisensor interpretation, mission planning, medical diagnosis, intelligence analysis, underwater vehicle tracking, antiair threat identification, robot vehicle navigation, and management decision support.

The National Information Infrastructure (NII) will have profound effects on the lives of every citizen. It promises to deliver to people in their homes and offices a vast array of information in many forms, changing the ways in which business is conducted, offering new educational opportunities, bringing geographically dispersed library resources and entertainment materials to everyone's doorstep. It will connect people to people, and help them with their jobs and tasks. For the NII to be useful, however, people will need easy and efficient access to its resources. Today's computers are complex and difficult to use, even for experts. The NII will be orders of magnitude more complex than current systems; it could easily become a labyrinth of databases and services that is inconvenient for experts and inaccessible to many Americans. The field of artificial intelligence (AI) can play a pivotal role in meeting major challenges of the NII. AI uses the theoretical and experimental tools of ...

It is important to divide the overall understanding process into manageable pieces.

In this paper, we develop a knowledge representation model for the innovative intelligent retrieval of legal cases, which provides effective legal case management. Examples are taken from the domain of accident compensation. A new set of sub-elements for legal case representation (sub-issues, pro-claimant, pro-respondent and contextual features) has been developed to extend the traditional representation elements of issues and factors. In our representation model, an issue may need to be further decomposed into sub-issues; factors are categorised into pro-claimant and pro-respondent factors; and contextual features are also introduced to help retrieval. These extensions can effectively reveal the factual relevance between legal cases.