TROFF to pdf or docx conversion?

I have my partially complete PhD thesis in TROFF format (?) - so it would laser print from the unix station back then. It has had a layer of Atari 1stWord encapsulation, which may still be at the header, but is essentially irrelevant as it was just a text editor to work on at home - away from the university's Sun workstations. Can anyone help me convert this to wysiwyg format of any sort please? pdf? docx? ¬066010303050000132002006006010000 ¬1¬¬¬1 ¬2¬#¬¬1 ¬F0110000001 ¬9[............................................................]0010 Ç .\" pic .\" tbl .\" eqn .\" roff -ms .\" .EQ delim $$ global .EN .ND .nr PO 1.5i .nr LL 6.0i .nr PS 9 .nr VS 18 .fp 1 H .TL .ps 14 CHAPTER 7 .AU .ps 14 KNOWLEDGE ENGINEERING .PP .ps 9 .NH Introduction .PP In the previous chapter we outlined a system which may be used to handle uncertainty in a domain, using probabilities on a causal network. In this chapter we will to discuss how the knowledge within a domain may be acquired and manipulated into the form of a probabilistic causal network - with specific reference to the two domains which represent the process of dating timber-framed buildings, using (i) architectural/historical and (ii) dendrochronological evidence. Our ultimate goal here is to ascertain, as best we may, the correct date of construction of vernacular buildings. As mentioned in Chapter 2, one of the main problems we face is the resolution of the potential conflict between dendrochronological and archaeological predictions for the date of a building. If the two bodies of knowledge produce results which are totally in agreement, we are left with no other conclusion than that the predicted date-range/period is the correct one, however there is rarely complete agreement and this disagreement may be very difficult to resolve. .PP We will derive networks on the two domains and give numerical Çexamples of propagation of evidence through these networks, closing the chapter with a short exposition of how we may use probabilistic methods in conjunction with causal networks to resolve this mentioned conflict. This use of probabilistic methods is contrary to widely adhered to notions concerning the unsuitability of this approach - from the general concerns over the epistemological adequacy of probabilistic methods in artificial intelligence expressed by McCarthy and Hayes (1969 p.490), to recent doubts about the implementational mechanics of probabilistic formalisms to be found in Graham and Llewelyn-Jones (1988 p.88 et seqq.).